<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:academictorrents="http://academictorrents.com/" version="2.0">
<channel>
<title>Journal of Machine Learning Research - Academic Torrents</title>
<description>collection curated by henryzlo</description>
<link>https://academictorrents.com/collection/journal-of-machine-learning-research</link>
<item>
<title>Learning Probabilistic Models: An Expected Utility Maximization Approach (Paper)</title>
<description>@article{4:11,author={Craig Friedman and Sven Sandow}, Title={Learning Probabilistic Models: An Expected Utility Maximization Approach},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/friedman03a/friedman03a.pdf}}</description>
<link>https://academictorrents.com/download/932f340dabe2e3416e210b9b62bfdb8bf6d7ffa3</link>
</item>
<item>
<title>Lossless Online Bayesian Bagging (Paper)</title>
<description>@article{5:5,author={Herbert K. H. Lee and Merlise A. Clyde}, Title={Lossless Online Bayesian Bagging},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/rifkin04a/rifkin04a.pdf}}</description>
<link>https://academictorrents.com/download/3505ee186a69f85a659c187209e896c10ee5af83</link>
</item>
<item>
<title>PREA: Personalized Recommendation Algorithms Toolkit (Paper)</title>
<description>@article{13:87,author={Joonseok Lee and Mingxuan Sun and Guy Lebanon}, Title={PREA: Personalized Recommendation Algorithms Toolkit},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/lee12b/lee12b.pdf}}</description>
<link>https://academictorrents.com/download/20c5722a541aa1ba00308b29f32edddbf8d99f28</link>
</item>
<item>
<title>Feature Selection via Dependence Maximization (Paper)</title>
<description>@article{13:47,author={Le Song and Alex Smola and Arthur Gretton and Justin Bedo and Karsten Borgwardt}, Title={Feature Selection via Dependence Maximization},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/song12a/song12a.pdf}}</description>
<link>https://academictorrents.com/download/cf7bf7de805b2266ded5349020a94a6670341096</link>
</item>
<item>
<title>Importance Sampling for Continuous Time Bayesian Networks (Paper)</title>
<description>@article{11:72,author={Yu Fan and Jing Xu and Christian R. Shelton}, Title={Importance Sampling for Continuous Time Bayesian Networks},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/fan10a/fan10a.pdf}}</description>
<link>https://academictorrents.com/download/1667047cab708a174b089171bfaa40245bd7f83b</link>
</item>
<item>
<title>Shark(Machine Learning Open Source Software Paper) (Paper)</title>
<description>@article{9:35,author={Eric Bax and Augusto Callejas}, Title={Shark(Machine Learning Open Source Software Paper)},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/munos08a/munos08a.pdf}}</description>
<link>https://academictorrents.com/download/0b19f22d3cd0e9736fa1c8da332e083432c265e9</link>
</item>
<item>
<title>Designing Committees of Models through Deliberate Weighting of Data Points (Paper)</title>
<description>@article{4:3,author={Stefan W. Christensen and Ian Sinclair and Philippa A. S. Reed}, Title={Designing Committees of Models through Deliberate Weighting of Data Points},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/christensen03a/christensen03a.pdf}}</description>
<link>https://academictorrents.com/download/234ab9d3e095f06f64a92ec1116b569e823463e8</link>
</item>
<item>
<title>Learning Evaluation Functions to Improve Optimization by Local Search (Paper)</title>
<description>@article{1:3,author={Justin Boyan and Andrew W. Moore}, Title={Learning Evaluation Functions to Improve Optimization by Local Search},journal={Journal of Machine Learning Research},volume={1}, url={http://www.jmlr.org/papers/volume1/boyan00a/boyan00a.pdf}}</description>
<link>https://academictorrents.com/download/1192c9b53b9b8e9d0770920ed80fbb079ac3996d</link>
</item>
<item>
<title>Facilitating Score and Causal Inference Trees for Large Observational Studies (Paper)</title>
<description>@article{13:95,author={Xiaogang Su and Joseph Kang and Juanjuan Fan and Richard A. Levine and Xin Yan}, Title={Facilitating Score and Causal Inference Trees for Large Observational Studies},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/su12a/su12a.pdf}}</description>
<link>https://academictorrents.com/download/0ee5b8cf1a70ba71aba9c80006ac5f2bb93ad498</link>
</item>
<item>
<title>Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels (Paper)</title>
<description>@article{3:31,author={Prasanth B. Nair and Arindam Choudhury and Andy J. Keane}, Title={Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/marchand02a/marchand02a.pdf}}</description>
<link>https://academictorrents.com/download/78694e1b7d119bf3f90c774a9d64c7397561fb05</link>
</item>
<item>
<title>Learnability, Stability and Uniform Convergence (Paper)</title>
<description>@article{11:90,author={Shai Shalev-Shwartz and Ohad Shamir and Nathan Srebro and Karthik Sridharan}, Title={Learnability, Stability and Uniform Convergence},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/shalev-shwartz10a/shalev-shwartz10a.pdf}}</description>
<link>https://academictorrents.com/download/3fa89ca74baca9a6183c78adcea426cc3f4bf3ef</link>
</item>
<item>
<title>Using Markov Blankets for Causal Structure Learning(Special Topic on Causality) (Paper)</title>
<description>@article{9:45,author={Zach Jorgensen and Yan Zhou and Meador Inge}, Title={Using Markov Blankets for Causal Structure Learning(Special Topic on Causality)},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/sabato08a/sabato08a.pdf}}</description>
<link>https://academictorrents.com/download/8e08ddb27ae589a238417b68e587dcb48350055b</link>
</item>
<item>
<title>On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation (Paper)</title>
<description>@article{11:70,author={Gavin C. Cawley and Nicola L. C. Talbot}, Title={On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/cawley10a/cawley10a.pdf}}</description>
<link>https://academictorrents.com/download/1fb1a2623f50c709710e610877d665ca1933fe01</link>
</item>
<item>
<title>Entropy Search for Information-Efficient Global Optimization (Paper)</title>
<description>@article{13:57,author={Philipp Hennig and Christian J. Schuler}, Title={Entropy Search for Information-Efficient Global Optimization},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/hennig12a/hennig12a.pdf}}</description>
<link>https://academictorrents.com/download/b05a6f20298620c47565f3a219372c7365d68fcb</link>
</item>
<item>
<title>Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming (Paper)</title>
<description>@article{13:13,author={Garvesh Raskutti and Martin J. Wainwright and Bin Yu}, Title={Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/raskutti12a/raskutti12a.pdf}}</description>
<link>https://academictorrents.com/download/77750329f24c8f314dd436be7b00bd6ec370ff2f</link>
</item>
<item>
<title>Local and Global Scaling Reduce Hubs in Space (Paper)</title>
<description>@article{13:92,author={Dominik Schnitzer and Arthur Flexer and Markus Schedl and Gerhard Widmer}, Title={Local and Global Scaling Reduce Hubs in Space},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/schnitzer12a/schnitzer12a.pdf}}</description>
<link>https://academictorrents.com/download/4308fad66470491613d8e4886126fd2482090480</link>
</item>
<item>
<title>Finding Recurrent Patterns from Continuous Sign Language Sentences for Automated Extraction of Signs (Paper)</title>
<description>@article{13:84,author={Sunita Nayak and Kester Duncan and Sudeep Sarkar and Barbara Loeding}, Title={Finding Recurrent Patterns from Continuous Sign Language Sentences for Automated Extraction of Signs},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/nayak12a/nayak12a.pdf}}</description>
<link>https://academictorrents.com/download/b37898e31c5ac61100e878ce252025c023470ad5</link>
</item>
<item>
<title>Introduction to Special Issue on Independent Components Analysis (Paper)</title>
<description>@article{4:46,author={Te-Won Lee and Jean-Franois Cardoso and Erkki Oja and Shun-ichi Amari}, Title={Introduction to Special Issue on Independent Components Analysis},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/malzahn03a/malzahn03a.pdf}}</description>
<link>https://academictorrents.com/download/57337b3b7e9018a8f640c5033bd6c77d238b03e8</link>
</item>
<item>
<title>A Compression Approach to Support Vector Model Selection (Paper)</title>
<description>@article{5:11,author={Ulrike von Luxburg and Olivier Bousquet and Bernhard
  Schlkopf}, Title={A Compression Approach to Support Vector Model Selection},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/baram04a/baram04a.pdf}}</description>
<link>https://academictorrents.com/download/88785a8c94e9ffa4fca127ae27f7a5109eb3648b</link>
</item>
<item>
<title>Special Issue on the Eighteenth International Conference on Machine Learning (ICML2001) (Paper)</title>
<description>@article{3:24,author={Carla E. Brodley and Andrea P. Danyluk}, Title={Special Issue on the Eighteenth International Conference on Machine Learning (ICML2001)},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/lanckriet02a/lanckriet02a.pdf}}</description>
<link>https://academictorrents.com/download/818692398ce10db04c3c86df13a5732212f65ef9</link>
</item>
<item>
<title>Continuous Time Bayesian Network Reasoning and Learning Engine (Paper)</title>
<description>@article{11:37,author={Christian R. Shelton and Yu Fan and William Lam and Joon Lee and Jing Xu}, Title={Continuous Time Bayesian Network Reasoning and Learning Engine},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/shelton10a/shelton10a.pdf}}</description>
<link>https://academictorrents.com/download/bfda57d28a4b8a0370bcc80bf096f64c08b6c3b6</link>
</item>
<item>
<title>Learning Semantic Lexicons from a Part-of-Speech and Semantically Tagged Corpus Using Inductive Logic Programming (Paper)</title>
<description>@article{4:22,author={Vincent Claveau and Pascale Sbillot and Ccile Fabre and Pierrette Bouillon}, Title={Learning Semantic Lexicons from a Part-of-Speech and Semantically Tagged Corpus Using Inductive Logic Programming},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/costa03a/costa03a.pdf}}</description>
<link>https://academictorrents.com/download/dd06605483247bc97dfdf2a688627f9552792557</link>
</item>
<item>
<title>A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss (Paper)</title>
<description>@article{13:91,author={Jos Hernndez-Orallo and Peter Flach and Csar Ferri}, Title={A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/hernandez-orallo12a/hernandez-orallo12a.pdf}}</description>
<link>https://academictorrents.com/download/dc07b26f8acd99dfc7d1763d46079930a1d1b4c5</link>
</item>
<item>
<title>Statistical Dynamics of On-line Independent Component Analysis (Paper)</title>
<description>@article{4:56,author={Gleb Basalyga and Magnus Rattray}, Title={Statistical Dynamics of On-line Independent Component Analysis},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/jang03a/jang03a.pdf}}</description>
<link>https://academictorrents.com/download/3b1322671d42bb6e951a700fad288137dcaefece</link>
</item>
<item>
<title>Latent Dirichlet Allocation (Paper)</title>
<description>@article{3:38,author={David M. Blei and Andrew Y. Ng and Michael I. Jordan}, Title={Latent Dirichlet Allocation},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/strens02a/strens02a.pdf}}</description>
<link>https://academictorrents.com/download/886506949d35110e5cfcf1cd5c5ffb16e0dc904b</link>
</item>
<item>
<title>Model Averaging for Prediction with Discrete Bayesian Networks (Paper)</title>
<description>@article{5:43,author={Denver Dash and Gregory F. Cooper}, Title={Model Averaging for Prediction with Discrete Bayesian Networks},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/chen04b/chen04b.pdf}}</description>
<link>https://academictorrents.com/download/65a8c28e71972ecba5d24fd655b627f1b2423960</link>
</item>
<item>
<title>On the Equivalence of Linear Dimensionality-Reducing Transformations (Paper)</title>
<description>@article{9:84,author={Eyal Krupka and Amir Navot and Naftali Tishby}, Title={On the Equivalence of Linear Dimensionality-Reducing Transformations},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/dhurandhar08a/dhurandhar08a.pdf}}</description>
<link>https://academictorrents.com/download/709afa4fc8581b99cd6f0991f6f660786f2326a3</link>
</item>
<item>
<title>Multi-Assignment Clustering for Boolean Data (Paper)</title>
<description>@article{13:15,author={Mario Frank and Andreas P. Streich and David Basin and Joachim M. Buhmann}, Title={Multi-Assignment Clustering for Boolean Data},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/frank12a/frank12a.pdf}}</description>
<link>https://academictorrents.com/download/df72006546f40f6fc8313fdec7dd4e10d4b913c1</link>
</item>
<item>
<title>A Kernel Two-Sample Test (Paper)</title>
<description>@article{13:25,author={Arthur Gretton and Karsten M. Borgwardt and Malte J. Rasch and Bernhard Schlkopf and Alexander Smola}, Title={A Kernel Two-Sample Test},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/gretton12a/gretton12a.pdf}}</description>
<link>https://academictorrents.com/download/234705eaac998d5c04b8e4c8a096c120d66404a0</link>
</item>
<item>
<title>On the Necessity of Irrelevant Variables (Paper)</title>
<description>@article{13:69,author={David P. Helmbold and Philip M. Long}, Title={On the Necessity of Irrelevant Variables},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/helmbold12a/helmbold12a.pdf}}</description>
<link>https://academictorrents.com/download/ffa02bdccbfd01ac5ce35c2bfee6210abb4ddd0f</link>
</item>
<item>
<title>A Local Spectral Method for Graphs: With Applications to Improving Graph Partitions and Exploring Data Graphs Locally (Paper)</title>
<description>@article{13:77,author={Michael W. Mahoney and Lorenzo Orecchia and Nisheeth K. Vishnoi}, Title={A Local Spectral Method for Graphs: With Applications to Improving Graph Partitions and Exploring Data Graphs Locally},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/mahoney12a/mahoney12a.pdf}}</description>
<link>https://academictorrents.com/download/5a0a7c5620fb0a6525dd940cff562869c737a978</link>
</item>
<item>
<title>Extensions to Metric-Based Model Selection (Paper)</title>
<description>@article{3:47,author={Yoshua Bengio and Nicolas Chapados}, Title={Extensions to Metric-Based Model Selection},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf}}</description>
<link>https://academictorrents.com/download/93a55ffbc30b6cfefaa4b665e979717e99d4405e</link>
</item>
<item>
<title>Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise (Paper)</title>
<description>@article{13:53,author={Sahand Negahban and Martin J. Wainwright}, Title={Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/negahban12a/negahban12a.pdf}}</description>
<link>https://academictorrents.com/download/d1c93e3859c2d0f6cc9a7e4ad1a6a04c28f2d5c9</link>
</item>
<item>
<title>Erratum: SGDQN is Less Careful than Expected (Paper)</title>
<description>@article{11:77,author={Antoine Bordes and Lon Bottou and Patrick Gallinari and Jonathan Chang and S. Alex Smith}, Title={Erratum: SGDQN is Less Careful than Expected},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/bordes10a/bordes10a.pdf}}</description>
<link>https://academictorrents.com/download/5ae1c9c1b06c251c154efefee93dcb23539d914e</link>
</item>
<item>
<title>Minimax Manifold Estimation (Paper)</title>
<description>@article{13:43,author={Christopher Genovese and Marco Perone-Pacifico and Isabella Verdinelli and Larry Wasserman}, Title={Minimax Manifold Estimation},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/genovese12a/genovese12a.pdf}}</description>
<link>https://academictorrents.com/download/5a779dec342b3228270697565c01547007ba131a</link>
</item>
<item>
<title>A Neural Probabilistic Language Model (Paper)</title>
<description>@article{3:44,author={Yoshua Bengio and Rjean Ducharme and Pascal Vincent and Christian Jauvin}, Title={A Neural Probabilistic Language Model},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/zelenko03a/zelenko03a.pdf}}</description>
<link>https://academictorrents.com/download/ccdfe60f5bb75ca85473c90d483e3802d53f5d12</link>
</item>
<item>
<title>Lagrangian Support Vector Machines (Kernel Machines Section) (Paper)</title>
<description>@article{1:6,author={O. L. Mangasarian and David R. Musicant}, Title={Lagrangian Support Vector Machines
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={1}, url={http://www.jmlr.org/papers/volume1/mangasarian01a/mangasarian01a.pdf}}</description>
<link>https://academictorrents.com/download/50df310af8d5acc4986cafae0cd716decb2c0592</link>
</item>
<item>
<title>Pairwise Support Vector Machines and their Application to Large Scale Problems (Paper)</title>
<description>@article{13:75,author={Carl Brunner and Andreas Fischer and Klaus Luig and Thorsten Thies}, Title={Pairwise Support Vector Machines and their Application to Large Scale Problems},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/brunner12a/brunner12a.pdf}}</description>
<link>https://academictorrents.com/download/d058dbfcc0abbcc7ea60eb1696f2d463f692a241</link>
</item>
<item>
<title>Lyapunov Design for Safe Reinforcement Learning (Paper)</title>
<description>@article{3:32,author={Theodore J. Perkins and Andrew G. Barto}, Title={Lyapunov Design for Safe Reinforcement Learning},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/marx02a/marx02a.pdf}}</description>
<link>https://academictorrents.com/download/622c9de43b3ce42daa46a9622e36d3e5bb2c1a21</link>
</item>
<item>
<title>Approximations for Binary Gaussian Process Classification (Paper)</title>
<description>@article{9:69,author={Mikio L. Braun and Joachim M. Buhmann and Klaus-Robert Mller}, Title={Approximations for Binary Gaussian Process Classification},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/fan08a/fan08a.pdf}}</description>
<link>https://academictorrents.com/download/23b7baf9db5d41de8f1f5f57e25f97c26a2637e6</link>
</item>
<item>
<title>Sally: A Tool for Embedding Strings in Vector Spaces (Paper)</title>
<description>@article{13:104,author={Konrad Rieck and Christian Wressnegger and Alexander Bikadorov}, Title={Sally: A Tool for Embedding Strings in Vector Spaces},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/rieck12a/rieck12a.pdf}}</description>
<link>https://academictorrents.com/download/acef731ff2e0ed888e7f860cb27c1417866c4f95</link>
</item>
<item>
<title>Rational Kernels: Theory and Algorithms (Special Topic on Learning Theory) (Paper)</title>
<description>@article{5:37,author={ Corinna Cortes and Patrick Haffner and Mehryar Mohri}, Title={Rational Kernels: Theory and Algorithms (Special Topic on Learning Theory)},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/christmann04a/christmann04a.pdf}}</description>
<link>https://academictorrents.com/download/64aacc7f046be66cfcc98e2c684bc5aa1c97d7ca</link>
</item>
<item>
<title>A Robust Minimax Approach to Classification (Paper)</title>
<description>@article{3:22,author={Gert R.G. Lanckriet and Laurent El Ghaoui and Chiranjib Bhattacharyya and Michael I. Jordan}, Title={A Robust Minimax Approach to Classification},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/chickering02b/chickering02b.pdf}}</description>
<link>https://academictorrents.com/download/bff8467895bf3ea2712ace7d87efb7a5f85b560b</link>
</item>
<item>
<title>No Unbiased Estimator of the Variance of K-Fold Cross-Validation (Paper)</title>
<description>@article{5:39,author={Yoshua Bengio and Yves Grandvalet}, Title={No Unbiased Estimator of the Variance of K-Fold Cross-Validation},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/sallans04a/sallans04a.pdf}}</description>
<link>https://academictorrents.com/download/cb8c8a3f743a330e249ef360ba6fa2147f4702ba</link>
</item>
<item>
<title>Knowledge-Based Kernel Approximation (Paper)</title>
<description>@article{5:41,author={Olvi L. Mangasarian and Jude W. Shavlik and Edward W. Wild}, Title={Knowledge-Based Kernel Approximation},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/kaariainen04a/kaariainen04a.pdf}}</description>
<link>https://academictorrents.com/download/501e017a78ab24b0063bd041f675af7675689b20</link>
</item>
<item>
<title>Regularized Discriminant Analysis, Ridge Regression and Beyond (Paper)</title>
<description>@article{11:76,author={Zhihua Zhang and Guang Dai and Congfu Xu and Michael I. Jordan}, Title={Regularized Discriminant Analysis, Ridge Regression and Beyond},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/zhang10b/zhang10b.pdf}}</description>
<link>https://academictorrents.com/download/e237f7aaaa9cd93a68b3abe3583c1bb75dcaad3d</link>
</item>
<item>
<title>Comments on the Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion (Paper)</title>
<description>@article{9:18,author={Eyal Krupka and Naftali Tishby}, Title={Comments on the Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/balakrishnan08a/balakrishnan08a.pdf}}</description>
<link>https://academictorrents.com/download/5aa0104d675b610e87a5b845a03140ea4657cc0d</link>
</item>
<item>
<title>On the Rate of Convergence of Regularized Boosting Classifiers (Paper)</title>
<description>@article{4:36,author={Gilles Blanchard and Gbor Lugosi and Nicolas Vayatis}, Title={On the Rate of Convergence of Regularized Boosting Classifiers},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/meir03a/meir03a.pdf}}</description>
<link>https://academictorrents.com/download/6df40c5b34af78337350e58acf68b7996400ca1e</link>
</item>
<item>
<title>Ultraconservative Online Algorithms for Multiclass Problems (Paper)</title>
<description>@article{3:37,author={Koby Crammer and Yoram Singer}, Title={Ultraconservative Online Algorithms for Multiclass Problems},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/singer02a/singer02a.pdf}}</description>
<link>https://academictorrents.com/download/2ea5a07cb4d042f1e177715b5104d8a4b3d78332</link>
</item>
<item>
<title>Nash Q-Learning for General-Sum Stochastic Games (Paper)</title>
<description>@article{4:42,author={Junling Hu and Michael P. Wellman}, Title={Nash Q-Learning for General-Sum Stochastic Games},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/zhong03a/zhong03a.pdf}}</description>
<link>https://academictorrents.com/download/4e78d4676e3609d7748288f77ae9a4df5732766f</link>
</item>
<item>
<title>R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning (Paper)</title>
<description>@article{3:9,author={Ronen I. Brafman and Moshe Tennenholtz}, Title={R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/herbrich02a/errata.pdf}}</description>
<link>https://academictorrents.com/download/3699dda91f82f2c6083f50166e3675779762ee93</link>
</item>
<item>
<title>Security Analysis of Online Centroid Anomaly Detection (Paper)</title>
<description>@article{13:118,author={Marius Kloft and Pavel Laskov}, Title={Security Analysis of Online Centroid Anomaly Detection},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/kloft12b/kloft12b.pdf}}</description>
<link>https://academictorrents.com/download/d3d9e74feb33847b8965f7c7a7e2fa0f2c30cfde</link>
</item>
<item>
<title>Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials (Paper)</title>
<description>@article{11:103,author={Rahul Gupta and Sunita Sarawagi and Ajit A. Diwan}, Title={Collective Inference for  Extraction MRFs Coupled with Symmetric Clique Potentials},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/gupta10a/gupta10a.pdf}}</description>
<link>https://academictorrents.com/download/e88fe7bda5fbabd26c7bbcb05a2c133bc54a789d</link>
</item>
<item>
<title>Exploration in Relational Domains for Model-based Reinforcement Learning (Paper)</title>
<description>@article{13:119,author={Tobias Lang and Marc Toussaint and Kristian Kersting}, Title={Exploration in Relational Domains for Model-based Reinforcement Learning},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/lang12a/lang12a.pdf}}</description>
<link>https://academictorrents.com/download/a13eed285b9f81acac1a8cbb4bedf91ce1524b76</link>
</item>
<item>
<title>Online Choice of Active Learning Algorithms (Paper)</title>
<description>@article{5:10,author={Yoram Baram and Ran El Yaniv and Kobi Luz}, Title={Online Choice of Active Learning Algorithms},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/basak04a/basak04a.pdf}}</description>
<link>https://academictorrents.com/download/8884920c4f634ffb624b921eb9046dfaecacf6b1</link>
</item>
<item>
<title>Algorithms for Sparse Linear Classifiers in the Massive Data Setting (Paper)</title>
<description>@article{9:12,author={Peter Bhlmann and Bin Yu}, Title={Algorithms for Sparse Linear Classifiers in the Massive Data Setting},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/bickel08a/bickel08a.pdf}}</description>
<link>https://academictorrents.com/download/a2a44d367d849fd323b3da394f52b1b4bbd38ae9</link>
</item>
<item>
<title>Composite Binary Losses (Paper)</title>
<description>@article{11:83,author={Mark D. Reid and Robert C. Williamson}, Title={Composite Binary Losses},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/reid10a/reid10a.pdf}}</description>
<link>https://academictorrents.com/download/ae3c57eec19b8e3217fef758e20b4461cda5bc0c</link>
</item>
<item>
<title>On-Line Sequential Bin Packing (Paper)</title>
<description>@article{11:4,author={Andrs Gyrgy and Gbor Lugosi and Gyrgy Ottucsk}, Title={On-Line Sequential Bin Packing},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/gyorgy10a/gyorgy10a.pdf}}</description>
<link>https://academictorrents.com/download/0495f04d838b46686431fd619f3e215bceaa2788</link>
</item>
<item>
<title>Quantum Set Intersection and its Application to Associative Memory (Paper)</title>
<description>@article{13:102,author={Tamer Salman and Yoram Baram}, Title={Quantum Set Intersection and its Application to Associative Memory},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/salman12a/salman12a.pdf}}</description>
<link>https://academictorrents.com/download/34a70fa34c879bf42656631c32a7712e99dcd028</link>
</item>
<item>
<title>Model-based Boosting 2.0 (Paper)</title>
<description>@article{11:71,author={Torsten Hothorn and Peter Bhlmann and Thomas Kneib and Matthias Schmid and Benjamin Hofner}, Title={Model-based Boosting 2.0},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/hothorn10a/hothorn10a.pdf}}</description>
<link>https://academictorrents.com/download/3cce95f965195c957591a4127581ae75070ec855</link>
</item>
<item>
<title>Covariance in Unsupervised Learning of Probabilistic Grammars (Paper)</title>
<description>@article{11:101,author={Shay B. Cohen and Noah A. Smith}, Title={Covariance in Unsupervised Learning of Probabilistic Grammars},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/cohen10a/cohen10a.pdf}}</description>
<link>https://academictorrents.com/download/6d65bd5ba3f10d7dd7759758cb94dbfd4b3e8677</link>
</item>
<item>
<title>Hit Miss Networks with Applications to Instance Selection (Paper)</title>
<description>@article{9:36,author={Mathias Drton and Thomas S. Richardson}, Title={Hit Miss Networks with Applications to Instance Selection},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/bax08a/bax08a.pdf}}</description>
<link>https://academictorrents.com/download/756bb7c2680231279d573ed63f0b1e24da2cea57</link>
</item>
<item>
<title>Lp-Nested Symmetric Distributions (Paper)</title>
<description>@article{11:111,author={Fabian Sinz and Matthias Bethge}, Title={Lp-Nested Symmetric Distributions},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/sinz10a/sinz10a.pdf}}</description>
<link>https://academictorrents.com/download/7f313160e766098db344b1656e2ed8789c49c69a</link>
</item>
<item>
<title>ILP: A Short Look Back and a Longer Look Forward (Paper)</title>
<description>@article{4:19,author={David Page and Ashwin Srinivasan}, Title={ILP: A Short Look Back and a Longer Look Forward},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/cussens03a/cussens03a.pdf}}</description>
<link>https://academictorrents.com/download/9ed45b61015c6141c2852e0cc4f63b978ee6f7eb</link>
</item>
<item>
<title>Inducing Tree-Substitution Grammars (Paper)</title>
<description>@article{11:102,author={Trevor Cohn and Phil Blunsom and Sharon Goldwater}, Title={Inducing Tree-Substitution Grammars},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/cohn10b/cohn10b.pdf}}</description>
<link>https://academictorrents.com/download/0f7ed39fca9f78a55a6902e64890856b66fcb5e4</link>
</item>
<item>
<title>Linear Algorithms for Online Multitask Classification (Paper)</title>
<description>@article{11:97,author={Giovanni Cavallanti and Nicol Cesa-Bianchi and Claudio Gentile}, Title={Linear Algorithms for Online Multitask Classification},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/cavallanti10a/cavallanti10a.pdf}}</description>
<link>https://academictorrents.com/download/e90de45a88ab4dc26492b4489f438d890281a288</link>
</item>
<item>
<title>A Convergent Online Single Time Scale Actor Critic Algorithm (Paper)</title>
<description>@article{11:11,author={Dotan Di Castro and Ron Meir}, Title={A Convergent Online Single Time Scale Actor Critic Algorithm},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/dicastro10a/dicastro10a.pdf}}</description>
<link>https://academictorrents.com/download/2d26acfe408e70b3c26efacea54735088bf82c65</link>
</item>
<item>
<title>Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels (Paper)</title>
<description>@article{11:44,author={Pinar Donmez and Guy Lebanon and Krishnakumar Balasubramanian}, Title={Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/donmez10a/donmez10a.pdf}}</description>
<link>https://academictorrents.com/download/0d23c6099604e45283962057800a6e42f6384b2d</link>
</item>
<item>
<title>Boosting as a Regularized Path to a Maximum Margin Classifier (Paper)</title>
<description>@article{5:34,author={ Saharon Rosset and Ji Zhu and Trevor Hastie}, Title={Boosting as a Regularized Path to a Maximum Margin Classifier},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/chen04a/chen04a.pdf}}</description>
<link>https://academictorrents.com/download/6bf88072c9021e637a8a624e79c15e70961ede1e</link>
</item>
<item>
<title>Support Vector Machine Soft Margin Classifiers: Error Analysis (Paper)</title>
<description>@article{5:42,author={Di-Rong Chen and Qiang Wu and Yiming Ying and Ding-Xuan Zhou}, Title={Support Vector Machine Soft Margin Classifiers: Error Analysis},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/mangasarian04a/mangasarian04a.pdf}}</description>
<link>https://academictorrents.com/download/aa95f64966993e878db72d2cc3aecaf1d475716b</link>
</item>
<item>
<title>Energy-Based Models for Sparse Overcomplete Representations (Paper)</title>
<description>@article{4:49,author={Yee Whye Teh and Max Welling and Simon Osindero and Geoffrey E. Hinton}, Title={Energy-Based Models for Sparse Overcomplete Representations},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/bach03a/bach03a.pdf}}</description>
<link>https://academictorrents.com/download/4108bc464fce4a222c2e26396eb6a628259044d3</link>
</item>
<item>
<title>Sufficient Dimensionality Reduction (Kernel Machines Section) (Paper)</title>
<description>@article{3:52,author={Amir Globerson and Naftali Tishby}, Title={Sufficient Dimensionality Reduction
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/dhillon03a/dhillon03a.pdf}}</description>
<link>https://academictorrents.com/download/8663822d38c2e62dfc0bc063e839688ba68088ed</link>
</item>
<item>
<title>FINkNN: A Fuzzy Interval Number k-Nearest Neighbor Classifier for Prediction of Sugar Production from Populations of Samples (Paper)</title>
<description>@article{4:2,author={Vassilios Petridis and Vassilis G. Kaburlasos}, Title={FINkNN: A Fuzzy Interval Number k-Nearest Neighbor Classifier for Prediction of Sugar Production from Populations of Samples},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/petridis03a/petridis03a.pdf}}</description>
<link>https://academictorrents.com/download/502de11e8c7df6ccbf8b37f8d1a1b516a2cd577d</link>
</item>
<item>
<title>Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting (Paper)</title>
<description>@article{11:12,author={Philippos Mordohai and Grard Medioni}, Title={Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/mordohai10a/mordohai10a.pdf}}</description>
<link>https://academictorrents.com/download/732e7896b2fdd062e94a459d6ba4f62ced4c7f29</link>
</item>
<item>
<title>PAC-Bayesian Analysis of Co-clustering and Beyond (Paper)</title>
<description>@article{11:117,author={Yevgeny Seldin and Naftali Tishby}, Title={PAC-Bayesian Analysis of Co-clustering and Beyond},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/seldin10a/seldin10a.pdf}}</description>
<link>https://academictorrents.com/download/449b5b080eb4f043034dc16497d31ee95b97acf7</link>
</item>
<item>
<title>Confidence-Weighted Linear Classification for Text Categorization (Paper)</title>
<description>@article{13:60,author={Koby Crammer and Mark Dredze and Fernando Pereira}, Title={Confidence-Weighted Linear Classification for Text Categorization},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/crammer12a/crammer12a.pdf}}</description>
<link>https://academictorrents.com/download/0d19898b3e3b4293787d1e7b210062161986be93</link>
</item>
<item>
<title>Eliminating Spammers and Ranking Annotators for Crowdsourced Labeling Tasks (Paper)</title>
<description>@article{13:16,author={Vikas C. Raykar and Shipeng Yu}, Title={Eliminating Spammers and Ranking Annotators for Crowdsourced Labeling Tasks},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/raykar12a/raykar12a.pdf}}</description>
<link>https://academictorrents.com/download/a9c761b751ed6ffbc5058ce97d767441844aaa08</link>
</item>
<item>
<title>Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies (Paper)</title>
<description>@article{9:10,author={Jerome Friedman and Trevor Hastie and Robert Tibshirani}, Title={Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/freund08a/freund08a.pdf}}</description>
<link>https://academictorrents.com/download/87f561d3d72af9b20f9d0298efddb88f948591db</link>
</item>
<item>
<title>Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data (Paper)</title>
<description>@article{11:32,author={Gideon S. Mann and Andrew McCallum}, Title={Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/mann10a/mann10a.pdf}}</description>
<link>https://academictorrents.com/download/bc809547166bcf9d0fb0bcb7b277ddaccf7c4f69</link>
</item>
<item>
<title>Preference Elicitation via Theory Refinement (Paper)</title>
<description>@article{4:14,author={Peter Haddawy and Vu Ha and Angelo Restificar and Benjamin Geisler and John Miyamoto}, Title={Preference Elicitation via Theory Refinement},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/haddawy03a/haddawy03a.pdf}}</description>
<link>https://academictorrents.com/download/d0c45984badd217ca59844462573dc5edec9574b</link>
</item>
<item>
<title>The Representational Power of Discrete Bayesian Networks (Paper)</title>
<description>@article{3:28,author={Charles X. Ling and Huajie Zhang}, Title={The Representational Power of Discrete Bayesian Networks},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/dooly02a/dooly02a.pdf}}</description>
<link>https://academictorrents.com/download/937e6389c6ace73d55931e39e9ae1e3d1ac6e72c</link>
</item>
<item>
<title>Transfer in Reinforcement Learning via Shared Features (Paper)</title>
<description>@article{13:45,author={George Konidaris and Ilya Scheidwasser and Andrew Barto}, Title={Transfer in Reinforcement Learning via Shared Features},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/konidaris12a/konidaris12a.pdf}}</description>
<link>https://academictorrents.com/download/ad7c56096190d25cd8e362b9a89e4b289b46c3b8</link>
</item>
<item>
<title>PAC-Bayes Bounds with Data Dependent Priors (Paper)</title>
<description>@article{13:112,author={Emilio Parrado-Hernndez and Amiran Ambroladze and John Shawe-Taylor and Shiliang Sun}, Title={PAC-Bayes Bounds with Data Dependent Priors},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/parrado12a/parrado12a.pdf}}</description>
<link>https://academictorrents.com/download/13efa078bace9fe68035ee3da1f60e4b3270e1e3</link>
</item>
<item>
<title>Limitations of Learning Via Embeddings in Euclidean Half Spaces (Paper)</title>
<description>@article{3:18,author={Shai Ben-David and Nadav Eiron and Hans Ulrich Simon}, Title={Limitations of Learning Via Embeddings in Euclidean Half Spaces},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/kalai02a/kalai02a.pdf}}</description>
<link>https://academictorrents.com/download/fd6c5f091409009d84c12d68d4dcc1f272a2ac95</link>
</item>
<item>
<title>Smoothing Multivariate Performance Measures (Paper)</title>
<description>@article{13:117,author={Xinhua Zhang and Ankan Saha and S.V.N. Vishwanathan}, Title={Smoothing Multivariate Performance Measures},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/zhang12d/zhang12d.pdf}}</description>
<link>https://academictorrents.com/download/821f225940a7aed9f6d38367cd54684bbd0f25a6</link>
</item>
<item>
<title>Concentration Inequalities for the Missing Mass and for Histogram Rule Error (Paper)</title>
<description>@article{4:37,author={David McAllester and Luis Ortiz}, Title={Concentration Inequalities for the Missing Mass and for Histogram Rule Error},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/blanchard03a/blanchard03a.pdf}}</description>
<link>https://academictorrents.com/download/584127b9189720f45039750333be5e5625a9c1ce</link>
</item>
<item>
<title>On the Importance of Small Coordinate Projections (Paper)</title>
<description>@article{5:8,author={Shahar Mendelson and Petra Philips}, Title={On the Importance of Small Coordinate Projections},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/anthony04a/anthony04a.pdf}}</description>
<link>https://academictorrents.com/download/75c3a5eaf6b12fd9a7b08a4e10d6ec784af30ae6</link>
</item>
<item>
<title>A Multiscale Framework For Blind Separation of Linearly Mixed Signals (Paper)</title>
<description>@article{4:54,author={Pavel Kisilev and Michael Zibulevsky and Yehoshua Y. Zeevi}, Title={A Multiscale Framework For Blind Separation of Linearly Mixed Signals},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/ziehe03a/ziehe03a.pdf}}</description>
<link>https://academictorrents.com/download/d63cbc0620adcf668aebd2f0274e9e8c2bc0a5d9</link>
</item>
<item>
<title>On the Rate of Convergence of the Bagged Nearest Neighbor Estimate (Paper)</title>
<description>@article{11:22,author={Grard Biau and Frdric Crou and Arnaud Guyader}, Title={On the Rate of Convergence of the Bagged Nearest Neighbor Estimate},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/biau10a/biau10a.pdf}}</description>
<link>https://academictorrents.com/download/8e2157d9ced05bfd778af368695c050027f37038</link>
</item>
<item>
<title>The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem (Paper)</title>
<description>@article{4:24,author={Pierre Baldi and Gianluca Pollastri}, Title={The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/castelo03a/castelo03a.pdf}}</description>
<link>https://academictorrents.com/download/03262bd503378b1d0c274a01d4022ad0ecb2de1d</link>
</item>
<item>
<title>Reinforcement Learning with Factored States and Actions (Paper)</title>
<description>@article{5:38,author={Brian Sallans and Geoffrey E. Hinton}, Title={Reinforcement Learning with Factored States and Actions},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/cortes04a/cortes04a.pdf}}</description>
<link>https://academictorrents.com/download/6776a581124c86f12e3aca0c4629f9e71adc45d6</link>
</item>
<item>
<title>Regret Bounds and Minimax Policies under Partial Monitoring (Paper)</title>
<description>@article{11:94,author={Jean-Yves Audibert and Sbastien Bubeck}, Title={Regret Bounds and Minimax Policies under Partial Monitoring},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/audibert10a/audibert10a.pdf}}</description>
<link>https://academictorrents.com/download/2113a0005716fde62b1201c4a2c846a615b30b66</link>
</item>
<item>
<title>Generalization from Observed to Unobserved Features by Clustering (Paper)</title>
<description>@article{9:13,author={David Mease and Abraham Wyner}, Title={Generalization from Observed to Unobserved Features by Clustering},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/buhlmann08a/buhlmann08a.pdf}}</description>
<link>https://academictorrents.com/download/e4d102e59cf6e3c68d0493b66b3c1ad809b11da3</link>
</item>
<item>
<title>A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning (Paper)</title>
<description>@article{11:39,author={Jin Yu and S.V.N. Vishwanathan and Simon Gunter and Nicol N. Schraudolph}, Title={A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/yu10a/yu10a.pdf}}</description>
<link>https://academictorrents.com/download/81d30aec29d668644d9c0b4e64bc41bdefa2d929</link>
</item>
<item>
<title>Algorithms for Learning Kernels Based on Centered Alignment (Paper)</title>
<description>@article{13:28,author={Corinna Cortes and Mehryar Mohri and Afshin Rostamizadeh}, Title={Algorithms for Learning Kernels Based on Centered Alignment},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/cortes12a/cortes12a.pdf}}</description>
<link>https://academictorrents.com/download/174f90e3b63899aa00e90012bd3e44ee5b79efb6</link>
</item>
<item>
<title>Stability Bounds for Stationary -mixing and -mixing Processes (Paper)</title>
<description>@article{11:26,author={Mehryar Mohri and Afshin Rostamizadeh}, Title={Stability Bounds for Stationary -mixing and -mixing Processes},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/mohri10a/mohri10a.pdf}}</description>
<link>https://academictorrents.com/download/dae7382244d21381a9a098efe009838ec491862b</link>
</item>
<item>
<title>Sparse Spectrum Gaussian Process Regression (Paper)</title>
<description>@article{11:63,author={Miguel Lzaro-Gredilla and Joaquin Quionero-Candela and Carl Edward Rasmussen and Anbal R. Figueiras-Vidal}, Title={Sparse Spectrum Gaussian Process Regression},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/lazaro-gredilla10a/lazaro-gredilla10a.pdf}}</description>
<link>https://academictorrents.com/download/510a89459197e881352e8a99ccea659083c3f96d</link>
</item>
<item>
<title>Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation (Paper)</title>
<description>@article{4:53,author={Andreas Ziehe and Motoaki Kawanabe and Stefan Harmeling and Klaus-Robert Mller}, Title={Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/almeida03a/almeida03a.pdf}}</description>
<link>https://academictorrents.com/download/2a0497807adea125d3c68cdc6a9c5e96473e8596</link>
</item>
<item>
<title>Least-Squares Policy Iteration (Paper)</title>
<description>@article{4:44,author={Michail G. Lagoudakis and Ronald Parr}, Title={Least-Squares Policy Iteration},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/steinwart03a/steinwart03a.pdf}}</description>
<link>https://academictorrents.com/download/2f194659087417e235adbaf606c9e170b7b6002c</link>
</item>
<item>
<title>Random Search for Hyper-Parameter Optimization (Paper)</title>
<description>@article{13:10,author={James Bergstra and Yoshua Bengio}, Title={Random Search for Hyper-Parameter Optimization},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf}}</description>
<link>https://academictorrents.com/download/57622727b8c7413fba521f635ade9ef36223023c</link>
</item>
<item>
<title>A Family of Additive Online Algorithms for Category Ranking (Paper)</title>
<description>@article{3:40,author={Koby Crammer and Yoram Singer}, Title={A Family of Additive Online Algorithms for Category Ranking},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf}}</description>
<link>https://academictorrents.com/download/f70052bb09cf26851208fc7b73d4dc8a5b710861</link>
</item>
<item>
<title>Path Kernels and Multiplicative Updates (Paper)</title>
<description>@article{4:33,author={Eiji Takimoto and Manfred K. Warmuth}, Title={Path Kernels and Multiplicative Updates},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/mendelson03a/mendelson03a.pdf}}</description>
<link>https://academictorrents.com/download/fe0610492c0ceb4f4013b0c95c7f349cec766f68</link>
</item>
<item>
<title>Max-margin Classification of Data with Absent Features (Paper)</title>
<description>@article{9:1,author={Gal Chechik and Geremy Heitz and Gal Elidan and Pieter Abbeel and Daphne Koller}, Title={Max-margin Classification of Data with Absent Features},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/chechik08a/chechik08a.pdf}}</description>
<link>https://academictorrents.com/download/10f362e8e840da581437cea0f6cbcbbc430d2887</link>
</item>
<item>
<title>Fast and Scalable Local Kernel Machines (Paper)</title>
<description>@article{11:64,author={Nicola Segata and Enrico Blanzieri}, Title={Fast and Scalable Local Kernel Machines},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/segata10a/segata10a.pdf}}</description>
<link>https://academictorrents.com/download/058438dc9c8f680864afbb8953e005c92f8da308</link>
</item>
<item>
<title>Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers (Paper)</title>
<description>@article{1:4,author={Erin L. Allwein and Robert E. Schapire and Yoram Singer}, Title={Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers},journal={Journal of Machine Learning Research},volume={1}, url={http://www.jmlr.org/papers/volume1/allwein00a/allwein00a.pdf}}</description>
<link>https://academictorrents.com/download/9886fcb86453efd5dd63496426ac3ce46ac5a5dd</link>
</item>
<item>
<title>Exact Bayesian Structure Discovery in Bayesian Networks (Paper)</title>
<description>@article{5:20,author={Mikko Koivisto and Kismat Sood}, Title={Exact Bayesian Structure Discovery in Bayesian Networks},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/langford04a/langford04a.pdf}}</description>
<link>https://academictorrents.com/download/24381f608b09bb20958184f73c895baf16522f47</link>
</item>
<item>
<title>Iterative Reweighted Algorithms for Matrix Rank Minimization (Paper)</title>
<description>@article{13:110,author={Karthik Mohan and Maryam Fazel}, Title={Iterative Reweighted Algorithms for Matrix Rank Minimization},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/mohan12a/mohan12a.pdf}}</description>
<link>https://academictorrents.com/download/24dbb7a0e4123b4b6782d4c2c8bcce583de40ff0</link>
</item>
<item>
<title>Classification Using Geometric Level Sets (Paper)</title>
<description>@article{11:14,author={Kush R. Varshney and Alan S. Willsky}, Title={Classification Using Geometric Level Sets},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/varshney10a/varshney10a.pdf}}</description>
<link>https://academictorrents.com/download/5237e8c321f52ece1a04f5e377b4c07d5ce85489</link>
</item>
<item>
<title>Mixability is Bayes Risk Curvature Relative to Log Loss (Paper)</title>
<description>@article{13:52,author={Tim van Erven and Mark D. Reid and Robert C. Williamson}, Title={Mixability is Bayes Risk Curvature Relative to Log Loss},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/vanerven12a/vanerven12a.pdf}}</description>
<link>https://academictorrents.com/download/aa86dcd4f400d55d2bbf9b3fe3c2ca5d3c177703</link>
</item>
<item>
<title>Consistent Nonparametric Tests of Independence (Paper)</title>
<description>@article{11:46,author={Arthur Gretton and Lszl Gyrfi}, Title={Consistent Nonparametric Tests of Independence},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/gretton10a/gretton10a.pdf}}</description>
<link>https://academictorrents.com/download/124c06f5f90001f63019dd5b2047d275a34bfc95</link>
</item>
<item>
<title>Probability Estimates for Multi-class Classification by Pairwise Coupling (Paper)</title>
<description>@article{5:35,author={ Ting-Fan Wu and Chih-Jen Lin and Ruby C. Weng}, Title={Probability Estimates for Multi-class Classification by Pairwise Coupling},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/rosset04a/rosset04a.pdf}}</description>
<link>https://academictorrents.com/download/07087e767df537c2df206dddb799d20abd627c22</link>
</item>
<item>
<title>Pattern for Python (Paper)</title>
<description>@article{13:66,author={Tom De Smedt and Walter Daelemans}, Title={Pattern for Python},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/desmedt12a/desmedt12a.pdf}}</description>
<link>https://academictorrents.com/download/fde4c9025c1cdba3606c63f5a70bc313c71d46a0</link>
</item>
<item>
<title>Linear Fitted-Q Iteration with Multiple Reward Functions (Paper)</title>
<description>@article{13:105,author={Daniel J. Lizotte and Michael Bowling and Susan A. Murphy}, Title={Linear Fitted-Q Iteration with Multiple Reward Functions},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/lizotte12a/lizotte12a.pdf}}</description>
<link>https://academictorrents.com/download/ab524298f818565f7b4d711895cc9f68cd2d442b</link>
</item>
<item>
<title>Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso (Paper)</title>
<description>@article{13:27,author={Rahul Mazumder and  Trevor Hastie}, Title={Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/mazumder12a/mazumder12a.pdf}}</description>
<link>https://academictorrents.com/download/83e025f04f12b8ede210a6e6de649563257bcd85</link>
</item>
<item>
<title>On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines (Paper)</title>
<description>@article{4:1,author={Aldebaro Klautau and Nikola Jevti and Alon Orlitsky}, Title={On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/klautau03a/klautau03a.pdf}}</description>
<link>https://academictorrents.com/download/0d0217867c557ff185fc893b6cf9b460c3580724</link>
</item>
<item>
<title>Topology Selection in Graphical Models of Autoregressive Processes (Paper)</title>
<description>@article{11:91,author={Jitkomut Songsiri and Lieven Vandenberghe}, Title={Topology Selection in Graphical Models of Autoregressive Processes},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/songsiri10a/songsiri10a.pdf}}</description>
<link>https://academictorrents.com/download/74af429ea8a9a70fc6e8628c967415b6b92b5084</link>
</item>
<item>
<title>Preference Elicitation and Query Learning (Special Topic on Learning Theory) (Paper)</title>
<description>@article{5:24,author={Avrim Blum and Jeffrey Jackson and Tuomas Sandholm and Martin
  Zinkevich}, Title={Preference Elicitation and Query Learning (Special Topic on Learning Theory)},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/mannor04b/mannor04b.pdf}}</description>
<link>https://academictorrents.com/download/1101a44824c650a0db007a929a626d81cdfcf5c6</link>
</item>
<item>
<title>Regularized Bundle Methods for Convex and Non-Convex Risks (Paper)</title>
<description>@article{13:114,author={Trinh Minh Tri Do and Thierry Artires}, Title={Regularized Bundle Methods for Convex and Non-Convex Risks},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/do12a/do12a.pdf}}</description>
<link>https://academictorrents.com/download/bbca1f9f0daac4327e0c4f6ad9e889b2b11fa6a9</link>
</item>
<item>
<title>Bouligand Derivatives and Robustness of Support Vector Machines for Regression (Paper)</title>
<description>@article{9:32,author={Jieping Ye and Shuiwang Ji and Jianhui Chen}, Title={Bouligand Derivatives and Robustness of Support Vector Machines for Regression},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/yoon08a/yoon08a.pdf}}</description>
<link>https://academictorrents.com/download/6716583910ceedf4985d3f1fd4e5fb6b8f53a242</link>
</item>
<item>
<title>GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression (Paper)</title>
<description>@article{13:26,author={Chiwoo Park and Jianhua Z. Huang and Yu Ding}, Title={GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/park12a/park12a.pdf}}</description>
<link>https://academictorrents.com/download/41860ce928ae47eb50b7dcd8170871277d2e1efa</link>
</item>
<item>
<title>Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions (Paper)</title>
<description>@article{11:8,author={Constantin F. Aliferis and Alexander Statnikov and Ioannis Tsamardinos and Subramani Mani and Xenofon D. Koutsoukos}, Title={Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/aliferis10b/aliferis10b.pdf}}</description>
<link>https://academictorrents.com/download/644188fbca51acc28a99ef5c6b5fc18e147e30e7</link>
</item>
<item>
<title>Bayes Point Machines (Kernel Machines Section) (Paper)</title>
<description>@article{1:9,author={Ralf Herbrich and Thore Graepel and Colin Campbell}, Title={Bayes Point Machines
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={1}, url={http://www.jmlr.org/papers/volume1/herbrich01a/herbrich01a.pdf}}</description>
<link>https://academictorrents.com/download/d1d353fe6c9e316ef89dc78a499bba2d2b7bb4ec</link>
</item>
<item>
<title>Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data (Paper)</title>
<description>@article{11:86,author={Milo Radovanovi and Alexandros Nanopoulos and Mirjana Ivanovi}, Title={Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/radovanovic10a/radovanovic10a.pdf}}</description>
<link>https://academictorrents.com/download/f0c816214fba191eb864acb4bc1139920972b00b</link>
</item>
<item>
<title>Graph Kernels (Paper)</title>
<description>@article{11:40,author={S.V.N. Vishwanathan and Nicol N. Schraudolph and Risi Kondor and Karsten M. Borgwardt}, Title={Graph Kernels},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/vishwanathan10a/vishwanathan10a.pdf}}</description>
<link>https://academictorrents.com/download/902c20ad640a17526110f45a87456118562f8fbb</link>
</item>
<item>
<title>Large-scale Linear Support Vector Regression (Paper)</title>
<description>@article{13:107,author={Chia-Hua Ho and Chih-Jen Lin}, Title={Large-scale Linear Support Vector Regression},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/ho12a/ho12a.pdf}}</description>
<link>https://academictorrents.com/download/b8af5361e7ddba127c6c19c56e49b8b4257b5c37</link>
</item>
<item>
<title>A Geometric Approach to Sample Compression (Paper)</title>
<description>@article{13:42,author={Benjamin I.P. Rubinstein and J. Hyam Rubinstein}, Title={A Geometric Approach to Sample Compression},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/rubinstein12a/rubinstein12a.pdf}}</description>
<link>https://academictorrents.com/download/83a478eded8871fdc9b9f5d6f04c3e62345d6250</link>
</item>
<item>
<title>A Topic Modeling Toolbox Using Belief Propagation (Paper)</title>
<description>@article{13:73,author={Jia Zeng}, Title={A Topic Modeling Toolbox Using Belief Propagation},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/zeng12a/zeng12a.pdf}}</description>
<link>https://academictorrents.com/download/9e9911232d0f2f7d5a4b87b1faa2411107786266</link>
</item>
<item>
<title>A Generalized Path Integral Control Approach to Reinforcement Learning (Paper)</title>
<description>@article{11:104,author={Evangelos Theodorou and Jonas Buchli and Stefan Schaal}, Title={A Generalized Path Integral Control Approach to Reinforcement Learning},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/theodorou10a/theodorou10a.pdf}}</description>
<link>https://academictorrents.com/download/0142723bea471e43f141538dacf4ed809e1756c6</link>
</item>
<item>
<title>Word-Sequence Kernels (Paper)</title>
<description>@article{3:41,author={Nicola Cancedda and Eric Gaussier and Cyril Goutte and Jean-Michel Renders}, Title={Word-Sequence Kernels},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/kandola03a/kandola03a.pdf}}</description>
<link>https://academictorrents.com/download/dfd1871e922ff4c2811882e3d719ea425cd189aa</link>
</item>
<item>
<title>Characterization, Stability and Convergence of Hierarchical Clustering Methods (Paper)</title>
<description>@article{11:47,author={Gunnar Carlsson and Facundo Mmoli}, Title={Characterization, Stability and Convergence of Hierarchical Clustering Methods},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/carlsson10a/carlsson10a.pdf}}</description>
<link>https://academictorrents.com/download/043cafaa559a5088f491fd6d29acadef767d252f</link>
</item>
<item>
<title>Image Denoising with Kernels Based on Natural Image Relations (Paper)</title>
<description>@article{11:29,author={Valero Laparra and Juan Gutirrez and Gustavo Camps-Valls and Jess Malo}, Title={Image Denoising with Kernels Based on Natural Image Relations},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/laparra10a/laparra10a.pdf}}</description>
<link>https://academictorrents.com/download/722798b9b0b8e12eec206ac3bf2844bceb6ab60f</link>
</item>
<item>
<title>Expectation Truncation and the Benefits of Preselection In Training Generative Models (Paper)</title>
<description>@article{11:96,author={Jrg Lcke and Julian Eggert}, Title={Expectation Truncation and the Benefits of Preselection In Training Generative Models},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/lucke10a/lucke10a.pdf}}</description>
<link>https://academictorrents.com/download/a7b797f1c372cb39ec23fd49fca4a82bf0e204ad</link>
</item>
<item>
<title>New Techniques for Disambiguation in Natural Language and Their Application to Biological Text (Paper)</title>
<description>@article{5:22,author={Filip Ginter and Jorma Boberg and Jouni Jrvinen and Tapio
  Salakoski}, Title={New Techniques for Disambiguation in Natural Language and Their Application
  to Biological Text},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/vovk04a/vovk04a.pdf}}</description>
<link>https://academictorrents.com/download/03476ad6223764d02fd63fc63e999d3c021b2840</link>
</item>
<item>
<title>Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms (Paper)</title>
<description>@article{1:11,author={Robert E. Mahony and Robert C. Williamson}, Title={Prior Knowledge and Preferential Structures in Gradient Descent Learning Algorithms},journal={Journal of Machine Learning Research},volume={1}, url={http://www.jmlr.org/papers/volume1/mahony01a/mahony01a.pdf}}</description>
<link>https://academictorrents.com/download/00d5583c3d2cef10043727f3fe893ee0d7da8591</link>
</item>
<item>
<title>Using Contextual Representations to Efficiently Learn Context-Free Languages (Paper)</title>
<description>@article{11:92,author={Alexander Clark and Rmi Eyraud and Amaury Habrard}, Title={Using Contextual Representations to Efficiently Learn Context-Free Languages},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/clark10a/clark10a.pdf}}</description>
<link>https://academictorrents.com/download/3855a22689cf7ff8aa35e59fae1a7dd36fc6943f</link>
</item>
<item>
<title>Sparse Semi-supervised Learning Using Conjugate Functions (Paper)</title>
<description>@article{11:84,author={Shiliang Sun and John Shawe-Taylor}, Title={Sparse Semi-supervised Learning Using Conjugate Functions},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/sun10a/sun10a.pdf}}</description>
<link>https://academictorrents.com/download/cddb9ae68147ba451d549738f0bfef2786237486</link>
</item>
<item>
<title>An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons (Paper)</title>
<description>@article{9:91,author={Yang-Bo He and Zhi Geng}, Title={An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/rakotomamonjy08a/rakotomamonjy08a.pdf}}</description>
<link>https://academictorrents.com/download/94ce5e5953a7438ff6e792ff93f22aef1ce1c02f</link>
</item>
<item>
<title>Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training (Paper)</title>
<description>@article{13:100,author={Zhuang Wang and Koby Crammer and Slobodan Vucetic}, Title={Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/wang12b/wang12b.pdf}}</description>
<link>https://academictorrents.com/download/dd455fdfb4bc66e32547d59dba4e95ef01bac8aa</link>
</item>
<item>
<title>Task Clustering and Gating for Bayesian Multitask Learning (Paper)</title>
<description>@article{4:5,author={Bart Bakker and Tom Heskes}, Title={Task Clustering and Gating for Bayesian Multitask Learning},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/bakker03a/bakker03a.pdf}}</description>
<link>https://academictorrents.com/download/6120c5133b7aa90e32951c74015d5edbc450788e</link>
</item>
<item>
<title>A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation (Paper)</title>
<description>@article{5:28,author={Andreas Ziehe and Pavel Laskov and Guido Nolte and Klaus-Robert Mller}, Title={A Fast Algorithm for Joint Diagonalization with Non-orthogonal Transformations and its Application to Blind Source Separation},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/valentini04a/valentini04a.pdf}}</description>
<link>https://academictorrents.com/download/1a66f047252e0beb2526052b1ec34ec4a291dc03</link>
</item>
<item>
<title>An Efficient Explanation of Individual Classifications using Game Theory (Paper)</title>
<description>@article{11:1,author={Erik trumbelj and Igor Kononenko}, Title={An Efficient Explanation of Individual Classifications using Game Theory},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/strumbelj10a/strumbelj10a.pdf}}</description>
<link>https://academictorrents.com/download/3397ba9d22c9d32db2a34cb7f54cd42af8d81594</link>
</item>
<item>
<title>A Recursive Method for Structural Learning of Directed Acyclic Graphs (Paper)</title>
<description>@article{9:16,author={Hsuan-Tien Lin and Ling Li}, Title={A Recursive Method for Structural Learning of Directed Acyclic Graphs},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/krause08a/krause08a.pdf}}</description>
<link>https://academictorrents.com/download/1639f8f3024d3007cb73b229cfea66fa561e3f2e</link>
</item>
<item>
<title>Refinement of Operator-valued Reproducing Kernels (Paper)</title>
<description>@article{13:4,author={Haizhang Zhang and Yuesheng Xu and Qinghui Zhang}, Title={Refinement of Operator-valued Reproducing Kernels},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/zhang12a/zhang12a.pdf}}</description>
<link>https://academictorrents.com/download/b509b5ce4211a0131fa8473ce008b9820b40763a</link>
</item>
<item>
<title>Evidence Contrary to the Statistical View of Boosting (Paper)</title>
<description>@article{9:6,author={David Mease and Abraham Wyner}, Title={Evidence Contrary to the Statistical View of Boosting},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/mease08a/mease08a.pdf}}</description>
<link>https://academictorrents.com/download/bd0aa8d48ad8b8e504f2a6f961d747743a597919</link>
</item>
<item>
<title>Approximate Tree Kernels (Paper)</title>
<description>@article{11:16,author={Konrad Rieck and Tammo Krueger and Ulf Brefeld and Klaus-Robert Mller}, Title={Approximate Tree Kernels},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/rieck10a/rieck10a.pdf}}</description>
<link>https://academictorrents.com/download/33c558a7bdaca3d68c9009a4789c23c370f54d4b</link>
</item>
<item>
<title>Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity (Paper)</title>
<description>@article{11:56,author={Aapo Hyvrinen and Kun Zhang and Shohei Shimizu and Patrik O. Hoyer}, Title={Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/hyvarinen10a/hyvarinen10a.pdf}}</description>
<link>https://academictorrents.com/download/ce87912ab6bf6bf6898f95309f0a21564a85091e</link>
</item>
<item>
<title>Tree Induction vs. Logistic Regression: A Learning-Curve Analysis (Paper)</title>
<description>@article{4:10,author={Claudia Perlich and Foster Provost and Jeffrey S. Simonoff}, Title={Tree Induction vs. Logistic Regression: A Learning-Curve Analysis},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/perlich03a/perlich03a.pdf}}</description>
<link>https://academictorrents.com/download/56e25fd7bb45197ace4a3cb3f6493878d1076924</link>
</item>
<item>
<title>Bayesian Mixed-Effects Inference on Classification Performance in Hierarchical Data Sets (Paper)</title>
<description>@article{13:101,author={Kay H. Brodersen and Christoph Mathys and Justin R. Chumbley and Jean Daunizeau and Cheng Soon Ong and Joachim M. Buhmann and Klaas E. Stephan}, Title={Bayesian Mixed-Effects Inference on Classification Performance in Hierarchical Data Sets},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/brodersen12a/brodersen12a.pdf}}</description>
<link>https://academictorrents.com/download/2e4f939686b1ee3662c375137ee2500d51308deb</link>
</item>
<item>
<title>Online Learning in the Embedded Manifold of Low-rank Matrices (Paper)</title>
<description>@article{13:14,author={Uri Shalit and Daphna Weinshall and Gal Chechik}, Title={Online Learning in the Embedded Manifold of Low-rank Matrices},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/shalit12a/shalit12a.pdf}}</description>
<link>https://academictorrents.com/download/6e74f34f9f7d264c65c7c5abab71bde6dfe3596a</link>
</item>
<item>
<title>Active Learning via Perfect Selective Classification (Paper)</title>
<description>@article{13:9,author={Ran El-Yaniv and Yair Wiener}, Title={Active Learning via Perfect Selective Classification},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/el-yaniv12a/el-yaniv12a.pdf}}</description>
<link>https://academictorrents.com/download/4df9c8a69217a388ad08e131f9f3135d1bfa0678</link>
</item>
<item>
<title>On Online Learning of Decision Lists (Paper)</title>
<description>@article{3:11,author={Ziv Nevo and Ran El-Yaniv}, Title={On Online Learning of Decision Lists},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/seeger02a/seeger02a.pdf}}</description>
<link>https://academictorrents.com/download/a6b41b30aaeebe94acc7ed4f4fa59f11f8fc8653</link>
</item>
<item>
<title>Variable Selection in High-dimensional Varying-coefficient Models with Global Optimality (Paper)</title>
<description>@article{13:63,author={Lan Xue and Annie Qu}, Title={Variable Selection in High-dimensional Varying-coefficient Models with Global Optimality},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/xue12a/xue12a.pdf}}</description>
<link>https://academictorrents.com/download/76cf546bf74aa2b8fac63cd4d8fa28b32a2db40f</link>
</item>
<item>
<title>Distributional Scaling: An Algorithm for Structure-Preserving Embedding of Metric and Nonmetric Spaces (Paper)</title>
<description>@article{5:14,author={Michael Quist and Golan Yona}, Title={Distributional Scaling: An Algorithm for Structure-Preserving Embedding of
  Metric and Nonmetric Spaces},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/lewis04a/lewis04a.pdf}}</description>
<link>https://academictorrents.com/download/412dfcfddc78602cb2c06cf7eed5024caacceb39</link>
</item>
<item>
<title>Bundle Methods for Regularized Risk Minimization (Paper)</title>
<description>@article{11:10,author={Choon Hui Teo and S.V.N. Vishwanthan and Alex J. Smola and Quoc V. Le}, Title={Bundle Methods for Regularized Risk Minimization},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/teo10a/teo10a.pdf}}</description>
<link>https://academictorrents.com/download/50af39c04117a844d8f46ac37602d26955d8702a</link>
</item>
<item>
<title>High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency (Paper)</title>
<description>@article{11:82,author={Dapo Omidiran and Martin J. Wainwright}, Title={High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/omidiran10a/omidiran10a.pdf}}</description>
<link>https://academictorrents.com/download/274969581d5f4d221b266d6c6b67acbfc491762c</link>
</item>
<item>
<title>A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification (Paper)</title>
<description>@article{11:105,author={Guo-Xun Yuan and Kai-Wei Chang and Cho-Jui Hsieh and Chih-Jen Lin}, Title={A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/yuan10c/yuan10c.pdf}}</description>
<link>https://academictorrents.com/download/e0342574371245947e145f49125d247966c42c76</link>
</item>
<item>
<title>Selective Rademacher Penalization and Reduced Error Pruning of Decision Trees (Paper)</title>
<description>@article{5:40,author={Matti Kriinen and Tuomo Malinen and Tapio Elomaa}, Title={Selective Rademacher Penalization and Reduced Error Pruning of Decision Trees},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/grandvalet04a/grandvalet04a.pdf}}</description>
<link>https://academictorrents.com/download/16e0cd092149a1d0d886146f917b411ee9bfe623</link>
</item>
<item>
<title>Selective Sampling and Active Learning from Single and Multiple Teachers (Paper)</title>
<description>@article{13:86,author={Ofer Dekel and Claudio Gentile and Karthik Sridharan}, Title={Selective Sampling and Active Learning from Single and Multiple Teachers},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/dekel12b/dekel12b.pdf}}</description>
<link>https://academictorrents.com/download/fb08d84af039c318a50f8406cd679ad90ba2d758</link>
</item>
<item>
<title>Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance (Paper)</title>
<description>@article{11:95,author={Nguyen Xuan Vinh and Julien Epps and James Bailey}, Title={Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/vinh10a/vinh10a.pdf}}</description>
<link>https://academictorrents.com/download/338e2e4bc75b07104e73e11440ca633eb144ce7b</link>
</item>
<item>
<title>Learning Bounded Treewidth Bayesian Networks (Paper)</title>
<description>@article{9:93,author={Laurens van der Maaten and Geoffrey Hinton}, Title={Learning Bounded Treewidth Bayesian Networks},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/fleuret08a/fleuret08a.pdf}}</description>
<link>https://academictorrents.com/download/5ef14ebb31914e28d839ce14a842d455fcdd6a30</link>
</item>
<item>
<title>Consistent Model Selection Criteria on High Dimensions (Paper)</title>
<description>@article{13:36,author={Yongdai Kim and Sunghoon Kwon and Hosik Choi}, Title={Consistent Model Selection Criteria on High Dimensions},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/kim12a/kim12a.pdf}}</description>
<link>https://academictorrents.com/download/f1f26d26cf9b7646221ac32cfd431bd9acf54cde</link>
</item>
<item>
<title>Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning (Paper)</title>
<description>@article{5:54,author={Evan Greensmith and Peter L. Bartlett and Jonathan Baxter}, Title={Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/hoyer04a/hoyer04a.pdf}}</description>
<link>https://academictorrents.com/download/b0da46a4d6018f022b5e22af6cd2b546e2d7aca4</link>
</item>
<item>
<title>Sampling Methods for the Nystrm Method (Paper)</title>
<description>@article{13:34,author={Sanjiv Kumar and Mehryar Mohri and Ameet Talwalkar}, Title={Sampling Methods for the Nystrm Method},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/kumar12a/kumar12a.pdf}}</description>
<link>https://academictorrents.com/download/be9f7827c593f14f5d2cdb28d395d90e867a29b4</link>
</item>
<item>
<title>Computable Shell Decomposition Bounds (Paper)</title>
<description>@article{5:19,author={John Langford and David McAllester}, Title={Computable Shell Decomposition Bounds},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/kauchak04a/kauchak04a.pdf}}</description>
<link>https://academictorrents.com/download/3f04847ac68a3ce462ee82f5d06e8147c88f3ea0</link>
</item>
<item>
<title>Learning Non-Stationary Dynamic Bayesian Networks (Paper)</title>
<description>@article{11:118,author={Joshua W. Robinson and Alexander J. Hartemink}, Title={Learning Non-Stationary Dynamic Bayesian Networks},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/robinson10a/robinson10a.pdf}}</description>
<link>https://academictorrents.com/download/657a539d78ca85231a4c30b7779e7f777d3e00ac</link>
</item>
<item>
<title>Posterior Regularization for Structured Latent Variable Models (Paper)</title>
<description>@article{11:67,author={Kuzman Ganchev and Joo Graa and Jennifer Gillenwater and Ben Taskar}, Title={Posterior Regularization for Structured Latent Variable Models},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/ganchev10a/ganchev10a.pdf}}</description>
<link>https://academictorrents.com/download/2b305b61748592f3960e5164c7a75e679acc48a3</link>
</item>
<item>
<title>Learning Algorithms for the Classification Restricted Boltzmann Machine (Paper)</title>
<description>@article{13:22,author={Hugo Larochelle and Michael Mandel and Razvan Pascanu and Yoshua Bengio}, Title={Learning Algorithms for the Classification Restricted Boltzmann Machine},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/larochelle12a/larochelle12a.pdf}}</description>
<link>https://academictorrents.com/download/7111107e9b410dcb938243037b1c2cab411ae0d0</link>
</item>
<item>
<title>Coherence Functions with Applications in Large-Margin Classification Methods (Paper)</title>
<description>@article{13:88,author={Zhihua Zhang and Dehua Liu and Guang Dai and Michael I. Jordan}, Title={Coherence Functions with Applications in Large-Margin Classification Methods},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/zhang12c/zhang12c.pdf}}</description>
<link>https://academictorrents.com/download/ee7e4e81f3992cbd660a706564c90dfbe7600ed1</link>
</item>
<item>
<title>On Spectral Learning (Paper)</title>
<description>@article{11:31,author={Andreas Argyriou and Charles A. Micchelli and Massimiliano Pontil}, Title={On Spectral Learning},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/argyriou10a/argyriou10a.pdf}}</description>
<link>https://academictorrents.com/download/c3967c3de128b6bfa1800879d4378495b0bc4032</link>
</item>
<item>
<title>Semi-Supervised Novelty Detection (Paper)</title>
<description>@article{11:99,author={Gilles Blanchard and Gyemin Lee and Clayton Scott}, Title={Semi-Supervised Novelty Detection},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/blanchard10a/blanchard10a.pdf}}</description>
<link>https://academictorrents.com/download/9fdd1dd5190e1360c2173cab897ebfc26480e3dd</link>
</item>
<item>
<title>Metric and Kernel Learning Using a Linear Transformation (Paper)</title>
<description>@article{13:17,author={Prateek Jain and Brian Kulis and Jason V. Davis and Inderjit S. Dhillon}, Title={Metric and Kernel Learning Using a Linear Transformation},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/jain12a/jain12a.pdf}}</description>
<link>https://academictorrents.com/download/5dec4c22172cf72a9ecf73b43d563e969ba40ed7</link>
</item>
<item>
<title>Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization (Paper)</title>
<description>@article{11:13,author={Jarkko Venna and Jaakko Peltonen and Kristian Nybo and Helena Aidos and Samuel Kaski}, Title={Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/venna10a/venna10a.pdf}}</description>
<link>https://academictorrents.com/download/fce657527452309b8424f8064c66ffe171d3c851</link>
</item>
<item>
<title>Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers (Paper)</title>
<description>@article{11:81,author={Franz Pernkopf and Jeff A. Bilmes}, Title={Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/pernkopf10a/pernkopf10a.pdf}}</description>
<link>https://academictorrents.com/download/1b022ef4ae73fcab4ec419909171e8b337615d0d</link>
</item>
<item>
<title>FastInf: An Efficient Approximate Inference Library (Paper)</title>
<description>@article{11:57,author={Ariel Jaimovich and Ofer Meshi and Ian McGraw and Gal Elidan}, Title={FastInf: An Efficient Approximate Inference Library},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/jaimovich10a/jaimovich10a.pdf}}</description>
<link>https://academictorrents.com/download/0d6ae43319679a7f2d46371b04e21d1a6b18595c</link>
</item>
<item>
<title>Learning Instance-Specific Predictive Models (Paper)</title>
<description>@article{11:109,author={Shyam Visweswaran and Gregory F. Cooper}, Title={Learning Instance-Specific Predictive Models},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/visweswaran10a/visweswaran10a.pdf}}</description>
<link>https://academictorrents.com/download/6d053654c5ef917bfa57f520a9960678b0bf87d4</link>
</item>
<item>
<title>Probability Product Kernels (Special Topic on Learning Theory) (Paper)</title>
<description>@article{5:30,author={Tony Jebara and Risi Kondor and Andrew Howard}, Title={Probability Product Kernels (Special Topic on Learning Theory)},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/laub04a/laub04a.pdf}}</description>
<link>https://academictorrents.com/download/99298a049a113961f3b4cba160ea30c73091c312</link>
</item>
<item>
<title>Fast Approximation of Matrix Coherence and Statistical Leverage (Paper)</title>
<description>@article{13:111,author={Petros Drineas and Malik Magdon-Ismail and Michael W. Mahoney and David P. Woodruff}, Title={Fast Approximation of Matrix Coherence and Statistical Leverage},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/drineas12a/drineas12a.pdf}}</description>
<link>https://academictorrents.com/download/20cb8b1f84a0405538ca7ea30ce8618f749ccef2</link>
</item>
<item>
<title>Introduction to the Special Issue on Machine Learning Methods for Text and Images (Paper)</title>
<description>@article{3:39,author={Jaz Kandola and Thomas Hofmann and Tomaso Poggio and John Shawe-Taylor}, Title={Introduction to the Special Issue on Machine Learning Methods for Text and Images},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/crammer03a/crammer03a.pdf}}</description>
<link>https://academictorrents.com/download/69af795cc5dd6387b7561fd20ff72615d802bb28</link>
</item>
<item>
<title>Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains (Paper)</title>
<description>@article{4:15,author={Helge Langseth and Thomas D. Nielsen}, Title={Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/langseth03a/langseth03a.pdf}}</description>
<link>https://academictorrents.com/download/f1ecf4fae4c705c7db6c7731b896c1f896f58d41</link>
</item>
<item>
<title>A Geometric Approach to Multi-Criterion Reinforcement Learning (Paper)</title>
<description>@article{5:12,author={Shie Mannor and Nahum Shimkin}, Title={A Geometric Approach to Multi-Criterion Reinforcement Learning},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/luxburg04a/luxburg04a.pdf}}</description>
<link>https://academictorrents.com/download/8dee57dd28d7a62ee2e35c68a9b8f04be69bcc69</link>
</item>
<item>
<title>Restricted Eigenvalue Properties for Correlated Gaussian Designs (Paper)</title>
<description>@article{11:78,author={Garvesh Raskutti and Martin J. Wainwright and Bin Yu}, Title={Restricted Eigenvalue Properties for Correlated Gaussian Designs},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/raskutti10a/raskutti10a.pdf}}</description>
<link>https://academictorrents.com/download/674c1f85278061a46d1a4a61a5234983c2abdbcc</link>
</item>
<item>
<title>Blind Source Recovery: A Framework in the State Space (Paper)</title>
<description>@article{4:57,author={Khurram Waheed and Fathi M. Salem}, Title={Blind Source Recovery: A Framework in the State Space},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/basalyga03a/basalyga03a.pdf}}</description>
<link>https://academictorrents.com/download/c4f729a7185a1a420bfe12f21db64289345a26d3</link>
</item>
<item>
<title>Algorithmic Luckiness (Paper)</title>
<description>@article{3:8,author={Ralf Herbrich and Robert C. Williamson}, Title={Algorithmic Luckiness},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/herbrich02a/herbrich02a.pdf}}</description>
<link>https://academictorrents.com/download/d71eaafbc22e4c039467db80d9c031f54e390627</link>
</item>
<item>
<title>Learning with Mixtures of Trees (Paper)</title>
<description>@article{1:1,author={Marina Meila and Michael I. Jordan}, Title={Learning with Mixtures of Trees},journal={Journal of Machine Learning Research},volume={1}, url={http://www.jmlr.org/papers/volume1/meila00a/meila00a.pdf}}</description>
<link>https://academictorrents.com/download/d2a046b69cc0f8f04a443f01f6b495e220b05b75</link>
</item>
<item>
<title>A Comparison of the Lasso and Marginal Regression (Paper)</title>
<description>@article{13:68,author={Christopher R. Genovese and Jiashun Jin and Larry Wasserman and Zhigang Yao}, Title={A Comparison of the Lasso and  Marginal Regression},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/genovese12b/genovese12b.pdf}}</description>
<link>https://academictorrents.com/download/b6126eb63713f8560bdf000c55e06e9b70570039</link>
</item>
<item>
<title>Algebraic Geometric Comparison of Probability Distributions (Paper)</title>
<description>@article{13:31,author={Franz J. Kirly and Paul von Bnau and Frank C. Meinecke and Duncan A.J. Blythe and Klaus-Robert Mller}, Title={Algebraic Geometric Comparison of Probability Distributions},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/kiraly12a/kiraly12a.pdf}}</description>
<link>https://academictorrents.com/download/a8ed169e525254fe11d75b824f2d1ba95b9e9643</link>
</item>
<item>
<title>Query Strategies for Evading Convex-Inducing Classifiers (Paper)</title>
<description>@article{13:44,author={Blaine Nelson and Benjamin I. P. Rubinstein and Ling Huang and Anthony D. Joseph and Steven J. Lee and Satish Rao and J. D. Tygar}, Title={Query Strategies for Evading Convex-Inducing Classifiers},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/nelson12a/nelson12a.pdf}}</description>
<link>https://academictorrents.com/download/f3bdf58e2eea2c35c9066a1e5fcbcd2b62d142e7</link>
</item>
<item>
<title>Sign Language Recognition using Sub-Units (Paper)</title>
<description>@article{13:72,author={Helen Cooper and Eng-Jon Ong and Nicolas Pugeault and Richard Bowden}, Title={Sign Language Recognition using Sub-Units},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/cooper12a/cooper12a.pdf}}</description>
<link>https://academictorrents.com/download/86bf966e9795896610e0d4609ad4b2ca489e3438</link>
</item>
<item>
<title>Why Does Unsupervised Pre-training Help Deep Learning? (Paper)</title>
<description>@article{11:19,author={Dumitru Erhan and Yoshua Bengio and Aaron Courville and Pierre-Antoine Manzagol and Pascal Vincent and Samy Bengio}, Title={Why Does Unsupervised Pre-training Help Deep Learning?},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/erhan10a/erhan10a.pdf}}</description>
<link>https://academictorrents.com/download/c03f42edc1ceb9f0d4a4fd259ef1ef0803aec192</link>
</item>
<item>
<title>A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives (Paper)</title>
<description>@article{13:50,author={Aleix Martinez and Shichuan Du}, Title={A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/martinez12a/martinez12a.pdf}}</description>
<link>https://academictorrents.com/download/0640d358d4e297c0ad855d874d554d83fd5606b4</link>
</item>
<item>
<title>Stability of Density-Based Clustering (Paper)</title>
<description>@article{13:32,author={Alessandro Rinaldo and Aarti Singh and Rebecca Nugent and Larry Wasserman}, Title={Stability of Density-Based Clustering},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/rinaldo12a/rinaldo12a.pdf}}</description>
<link>https://academictorrents.com/download/fdce3a5899f58bdae0f8a06105576ddc81eb89e1</link>
</item>
<item>
<title>Regularized Principal Manifolds (Kernel Machines Section) (Paper)</title>
<description>@article{1:7,author={Alexander J. Smola and Sebastian Mika and Bernhard Schlkopf and Robert C. Williamson}, Title={Regularized Principal Manifolds
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={1}, url={http://www.jmlr.org/papers/volume1/smola01a/smola01a.pdf}}</description>
<link>https://academictorrents.com/download/54fde50d7ead3bddc030d1b3c9348d89e698a1d2</link>
</item>
<item>
<title>Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods (Paper)</title>
<description>@article{5:27,author={Giorgio Valentini and Thomas G. Dietterich}, Title={Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/zhang04a/zhang04a.pdf}}</description>
<link>https://academictorrents.com/download/c741c15c75d92ec8b37cd9b2d3ef157f3acde647</link>
</item>
<item>
<title>Subgroup Discovery with CN2-SD (Paper)</title>
<description>@article{5:6,author={Nada Lavra and Branko Kavek and Peter Flach and Ljupo
  Todorovski}, Title={Subgroup Discovery with CN2-SD},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/lee04a/lee04a.pdf}}</description>
<link>https://academictorrents.com/download/5c7183e8c3f23ead7dcdfc70340120d312e779f9</link>
</item>
<item>
<title>Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications (Paper)</title>
<description>@article{13:58,author={Jian Huang and Cun-Hui Zhang}, Title={Estimation and Selection via Absolute Penalized Convex Minimization And Its Multistage Adaptive Applications},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/huang12b/huang12b.pdf}}</description>
<link>https://academictorrents.com/download/12dec74bab119eaa23a0ced63349ba6e35cf8656</link>
</item>
<item>
<title>Consensus-Based Distributed Support Vector Machines (Paper)</title>
<description>@article{11:55,author={Pedro A. Forero and Alfonso Cano and Georgios B. Giannakis}, Title={Consensus-Based Distributed Support Vector Machines},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/forero10a/forero10a.pdf}}</description>
<link>https://academictorrents.com/download/4194b6f476aedf6b101d5e4d3a99159ac9d5c076</link>
</item>
<item>
<title>Non-Sparse Multiple Kernel Fisher Discriminant Analysis (Paper)</title>
<description>@article{13:21,author={Fei Yan and Josef Kittler and Krystian Mikolajczyk and Atif Tahir}, Title={Non-Sparse Multiple Kernel Fisher Discriminant Analysis},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/yan12a/yan12a.pdf}}</description>
<link>https://academictorrents.com/download/c3eee4a1e43d2ab0d8cf860eb77f3d2af076bd5e</link>
</item>
<item>
<title>The huge Package for High-dimensional Undirected Graph Estimation in R (Paper)</title>
<description>@article{13:37,author={Tuo Zhao and Han Liu and Kathryn Roeder and John Lafferty and Larry Wasserman}, Title={The huge Package for High-dimensional Undirected Graph Estimation in R},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/zhao12a/zhao12a.pdf}}</description>
<link>https://academictorrents.com/download/b33984a3ffa7a931e34d2af393becbeda77c2bf1</link>
</item>
<item>
<title>Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion (Paper)</title>
<description>@article{11:110,author={Pascal Vincent and Hugo Larochelle and Isabelle Lajoie and Yoshua Bengio and Pierre-Antoine Manzagol}, Title={Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/vincent10a/vincent10a.pdf}}</description>
<link>https://academictorrents.com/download/4455093da9243e81e4d23bcef57178a917f6c183</link>
</item>
<item>
<title>ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel (Paper)</title>
<description>@article{13:19,author={Stephen R. Piccolo and Lewis J. Frey}, Title={ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/piccolo12a/piccolo12a.pdf}}</description>
<link>https://academictorrents.com/download/204a7af1ec7aafcf1c7a37102d4b353a93620f2d</link>
</item>
<item>
<title>MOA: Massive Online Analysis (Paper)</title>
<description>@article{11:52,author={Albert Bifet and Geoff Holmes and Richard Kirkby and Bernhard Pfahringer}, Title={MOA: Massive Online Analysis},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/bifet10a/bifet10a.pdf}}</description>
<link>https://academictorrents.com/download/fa2f814f97e6425ee42f63859c96ff4f80002919</link>
</item>
<item>
<title>Dimensionality Reduction via Sparse Support Vector Machines (Kernel Machines Section) (Paper)</title>
<description>@article{3:48,author={Jinbo Bi and Kristin Bennett and Mark Embrechts and Curt Breneman and Minghu Song}, Title={Dimensionality Reduction via Sparse Support Vector Machines
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/bekkerman03a/bekkerman03a.pdf}}</description>
<link>https://academictorrents.com/download/fdb1fb6a9897eb597328eaa81b648bd8fc5d1ec0</link>
</item>
<item>
<title>Introduction to the Special Issue on the Fusion of Domain Knowledge with Data for Decision Support (Paper)</title>
<description>@article{4:12,author={Richard Dybowski and Kathryn B. Laskey and James W. Myers and Simon Parsons}, Title={Introduction to the Special Issue on the Fusion of Domain Knowledge with Data for Decision Support},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/dybowski03a/dybowski03a.pdf}}</description>
<link>https://academictorrents.com/download/58d212578d6b6432a113f6dd3ef0b1a3c528cf9f</link>
</item>
<item>
<title>Learning From Crowds (Paper)</title>
<description>@article{11:43,author={Vikas C. Raykar and Shipeng Yu and Linda H. Zhao and Gerardo Hermosillo Valadez and Charles Florin and Luca Bogoni and Linda Moy}, Title={Learning From Crowds},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/raykar10a/raykar10a.pdf}}</description>
<link>https://academictorrents.com/download/c7e94383a95f855df08531e30bfcb717924baa8b</link>
</item>
<item>
<title>Rate Minimaxity of the Lasso and Dantzig Selector for the lq Loss in lr Balls (Paper)</title>
<description>@article{11:114,author={Fei Ye and Cun-Hui Zhang}, Title={Rate Minimaxity of the Lasso and Dantzig Selector for the lq Loss in lr Balls},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/ye10a/ye10a.pdf}}</description>
<link>https://academictorrents.com/download/ba4ad580df0ca2517cd8ea20551bc2645bbf573f</link>
</item>
<item>
<title>SVDFeature: A Toolkit for Feature-based Collaborative Filtering (Paper)</title>
<description>@article{13:116,author={Tianqi Chen and Weinan Zhang and Qiuxia Lu and Kailong Chen and Zhao Zheng and Yong Yu}, Title={SVDFeature: A Toolkit for Feature-based Collaborative Filtering},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/chen12a/chen12a.pdf}}</description>
<link>https://academictorrents.com/download/39fba4740d1db0ce4c2a28ea41c7e1e4d609a728</link>
</item>
<item>
<title>ICA for Watermarking Digital Images (Paper)</title>
<description>@article{4:59,author={Stphane Bounkong and Bormi Toch and David Saad and David Lowe}, Title={ICA for Watermarking Digital Images},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/sarela03a/sarela03a.pdf}}</description>
<link>https://academictorrents.com/download/9f40e4061d1c70a8ef6e0c828c1f7f4f4b64648b</link>
</item>
<item>
<title>A Generative Model for Separating Illumination and Reflectance from Images (Paper)</title>
<description>@article{4:60,author={Inna Stainvas and David Lowe}, Title={A Generative Model for Separating Illumination and Reflectance from Images},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/bounkong03a/bounkong03a.pdf}}</description>
<link>https://academictorrents.com/download/58f00d4652cd7b5b5a238c5562a977a39183defa</link>
</item>
<item>
<title>Active Clustering of Biological Sequences (Paper)</title>
<description>@article{13:7,author={Konstantin Voevodski and Maria-Florina Balcan and Heiko Rglin and Shang-Hua Teng and Yu Xia}, Title={Active Clustering of Biological Sequences},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/voevodski12a/voevodski12a.pdf}}</description>
<link>https://academictorrents.com/download/9dd1a91e17e2294624f643e3f1073cb26aa39a2c</link>
</item>
<item>
<title>Learning Rates for Q-learning (Paper)</title>
<description>@article{5:1,author={Eyal Even Dar and Yishay Mansour}, Title={Learning Rates for Q-learning},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/evendar03a/evendar03a.pdf}}</description>
<link>https://academictorrents.com/download/3459786202aa807fde4498d1b85fad02d314d94a</link>
</item>
<item>
<title>A Rotation Test to Verify Latent Structure (Paper)</title>
<description>@article{11:18,author={Patrick O. Perry and Art B. Owen}, Title={A Rotation Test to Verify Latent Structure},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/perry10a/perry10a.pdf}}</description>
<link>https://academictorrents.com/download/20034c6750ff4bc08205ca738a7a4c8e284c2025</link>
</item>
<item>
<title>Tracking a Small Set of Experts by Mixing Past Posteriors (Paper)</title>
<description>@article{3:15,author={Olivier Bousquet and Manfred K. Warmuth}, Title={Tracking a Small Set of Experts by Mixing Past Posteriors},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/long02a/long02a.pdf}}</description>
<link>https://academictorrents.com/download/31c61e72eab7361db5abfae6e0ba8dfb5140e3a4</link>
</item>
<item>
<title>Dependency Networks for Inference, Collaborative Filtering, and Data Visualization (Paper)</title>
<description>@article{1:2,author={David Heckerman and David Maxwell Chickering and Christopher Meek and Robert Rounthwaite and Carl Kadie}, Title={Dependency Networks for Inference, Collaborative Filtering, and Data Visualization},journal={Journal of Machine Learning Research},volume={1}, url={http://www.jmlr.org/papers/volume1/heckerman00a/heckerman00a.pdf}}</description>
<link>https://academictorrents.com/download/d8552c6f745b8000a052c9fcb09635e97bc63521</link>
</item>
<item>
<title>Introduction to the Special Issue on Inductive Logic Programming (Paper)</title>
<description>@article{4:18,author={James Cussens and Alan M. Frisch}, Title={Introduction to the Special Issue on Inductive Logic Programming},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/gadanho03a/gadanho03a.pdf}}</description>
<link>https://academictorrents.com/download/e5383c8d7ddc65c489aedfd1fbbc73f053c81f87</link>
</item>
<item>
<title>Oger: Modular Learning Architectures For Large-Scale Sequential Processing (Paper)</title>
<description>@article{13:96,author={David Verstraeten and Benjamin Schrauwen and Sander Dieleman and Philemon Brakel and Pieter Buteneers and Dejan Pecevski}, Title={Oger: Modular Learning Architectures For Large-Scale Sequential Processing},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/verstraeten12a/verstraeten12a.pdf}}</description>
<link>https://academictorrents.com/download/6c1060c9dca64d64c7f633f9a8b03cdf7beed258</link>
</item>
<item>
<title>Query Transformations for Improving the Efficiency of ILP Systems (Paper)</title>
<description>@article{4:21,author={Vtor Santos Costa and Ashwin Srinivasan and Rui Camacho and Hendrik Blockeel and Bart Demoen and Gerda Janssens and Jan Struyf and Henk Vandecasteele and Wim Van Laer}, Title={Query Transformations for Improving the Efficiency of ILP Systems},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/botta03a/botta03a.pdf}}</description>
<link>https://academictorrents.com/download/d15b116c906d3995979b99e5f143658e60327e84</link>
</item>
<item>
<title>Efficient Algorithms for Universal Portfolios (Paper)</title>
<description>@article{3:17,author={Adam Kalai and Santosh Vempala}, Title={Efficient Algorithms for Universal Portfolios},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/auer02a/auer02a.pdf}}</description>
<link>https://academictorrents.com/download/c44653bbc9769fd294d332795c3f0c85a658617b</link>
</item>
<item>
<title>A Maximum Likelihood Approach to Single-channel Source Separation (Paper)</title>
<description>@article{4:55,author={Gil-Jin Jang and Te-Won Lee}, Title={A Maximum Likelihood Approach to Single-channel Source Separation},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/kisilev03a/kisilev03a.pdf}}</description>
<link>https://academictorrents.com/download/aa50815201cdf7090d85b151413fe33009ad4075</link>
</item>
<item>
<title>Permutation Tests for Studying Classifier Performance (Paper)</title>
<description>@article{11:62,author={Markus Ojala and Gemma C. Garriga}, Title={Permutation Tests for Studying Classifier Performance},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/ojala10a/ojala10a.pdf}}</description>
<link>https://academictorrents.com/download/6fc40198ba0ab0706759c533a28001804a377c8b</link>
</item>
<item>
<title>On the Foundations of Noise-free Selective Classification (Paper)</title>
<description>@article{11:53,author={Ran El-Yaniv and Yair Wiener}, Title={On the Foundations of Noise-free Selective Classification},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/el-yaniv10a/el-yaniv10a.pdf}}</description>
<link>https://academictorrents.com/download/d6ba8863427fcdd7f8f73de75385ee61c3dbf0e4</link>
</item>
<item>
<title>PyBrain (Paper)</title>
<description>@article{11:24,author={Tom Schaul and Justin Bayer and Daan Wierstra and Yi Sun and Martin Felder and Frank Sehnke and Thomas Rckstie and Jrgen Schmidhuber}, Title={PyBrain},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/schaul10a/schaul10a.pdf}}</description>
<link>https://academictorrents.com/download/22aeae981371179668f128d97c62ac9f4435f605</link>
</item>
<item>
<title>On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition (Paper)</title>
<description>@article{5:36,author={ Andreas Christmann and Ingo Steinwart}, Title={On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/wu04a/wu04a.pdf}}</description>
<link>https://academictorrents.com/download/59655ff68200d472c491cc2b8b8112275e3ff3f6</link>
</item>
<item>
<title>Speedup Learning for Repair-based Search by Identifying Redundant Steps (Paper)</title>
<description>@article{4:27,author={Shaul Markovitch and Asaf Shatil}, Title={Speedup Learning for Repair-based Search by Identifying Redundant Steps},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/servedio03a/servedio03a.pdf}}</description>
<link>https://academictorrents.com/download/35f46d5b8f8f76d4e211ecdda423242b6057c382</link>
</item>
<item>
<title>The em Algorithm for Kernel Matrix Completion with Auxiliary Data (Paper)</title>
<description>@article{4:4,author={Koji Tsuda and Shotaro Akaho and Kiyoshi Asai}, Title={The em Algorithm for Kernel Matrix Completion with Auxiliary Data},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/tsuda03a/tsuda03a.pdf}}</description>
<link>https://academictorrents.com/download/8056737d81e1a9ff89e3b6566a7f69aa37a60e09</link>
</item>
<item>
<title>Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints (Paper)</title>
<description>@article{13:81,author={Mehrdad Mahdavi and Rong Jin and Tianbao Yang}, Title={Trading Regret for Efficiency: Online Convex Optimization with Long Term Constraints},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/mahdavi12a/mahdavi12a.pdf}}</description>
<link>https://academictorrents.com/download/ed4dd1fe07fa81d93667cb6c6adf9ce6147f4ed5</link>
</item>
<item>
<title>Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases (Paper)</title>
<description>@article{11:73,author={Guoqiang Yu and Yuanjian Feng and David J. Miller and Jianhua Xuan and Eric P. Hoffman and Robert Clarke and Ben Davidson and Ie-Ming Shih and Yue Wang}, Title={Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/yu10b/yu10b.pdf}}</description>
<link>https://academictorrents.com/download/7f9333199bf3ce69d5e58e7f7cc86489ed4c6c98</link>
</item>
<item>
<title>Classification Methods with Reject Option Based on Convex Risk Minimization (Paper)</title>
<description>@article{11:5,author={Ming Yuan and Marten Wegkamp}, Title={Classification Methods with Reject Option Based on Convex Risk Minimization},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/yuan10a/yuan10a.pdf}}</description>
<link>https://academictorrents.com/download/26cdc093869bf1be51b954dec56c5ec2c3fdbc93</link>
</item>
<item>
<title>Some Properties of Regularized Kernel Methods (Paper)</title>
<description>@article{5:49,author={Ernesto De Vito and Lorenzo Rosasco and Andrea Caponnetto and Michele Piana and Alessandro Verri}, Title={Some Properties of Regularized Kernel Methods},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/stracuzzi04a/stracuzzi04a.pdf}}</description>
<link>https://academictorrents.com/download/930280d0918a2b8941c035ccf0aa642e8cfe1152</link>
</item>
<item>
<title>An Exponential Model for Infinite Rankings (Paper)</title>
<description>@article{11:113,author={Marina Meil and Le Bao}, Title={An Exponential Model for Infinite Rankings},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/meila10a/meila10a.pdf}}</description>
<link>https://academictorrents.com/download/6db71aa8d73c2cc65af35047167008da0a746d08</link>
</item>
<item>
<title>Evolving Static Representations for Task Transfer (Paper)</title>
<description>@article{11:58,author={Phillip Verbancsics and Kenneth O. Stanley}, Title={Evolving Static Representations for Task Transfer},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/verbancsics10a/verbancsics10a.pdf}}</description>
<link>https://academictorrents.com/download/efdec7ba0c0770923e79325ab0e34c20784d9279</link>
</item>
<item>
<title>RCV1: A New Benchmark Collection for Text Categorization Research (Paper)</title>
<description>@article{5:13,author={David D. Lewis and Yiming Yang and Tony G. Rose and Fan Li}, Title={RCV1: A New Benchmark Collection for Text Categorization Research},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/mannor04a/mannor04a.pdf}}</description>
<link>https://academictorrents.com/download/7e86d470f7a4cd0b370c141b7ed6cf6ca7ff170c</link>
</item>
<item>
<title>Optimal Solutions for Sparse Principal Component Analysis (Paper)</title>
<description>@article{9:44,author={Sivan Sabato and Shai Shalev-Shwartz}, Title={Optimal Solutions for Sparse Principal Component Analysis},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/maurer08a/maurer08a.pdf}}</description>
<link>https://academictorrents.com/download/f95e296ad0515cd337e57a3df881f37763e54eae</link>
</item>
<item>
<title>Generalization Error Bounds for Bayesian Mixture Algorithms (Paper)</title>
<description>@article{4:35,author={Ron Meir and Tong Zhang}, Title={Generalization Error Bounds for Bayesian Mixture Algorithms},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/mesterharm03a/mesterharm03a.pdf}}</description>
<link>https://academictorrents.com/download/b859c3d5df15627e2bbce1b49b2ce0060d23a55f</link>
</item>
<item>
<title>Model Selection: Beyond the BayesianFrequentist Divide (Paper)</title>
<description>@article{11:3,author={Isabelle Guyon and Amir Saffari and Gideon Dror and Gavin Cawley}, Title={Model Selection: Beyond the BayesianFrequentist Divide},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/guyon10a/guyon10a.pdf}}</description>
<link>https://academictorrents.com/download/ae655426e6cb2851f2bc3d75d41ee454acf2ac5a</link>
</item>
<item>
<title>Relational Learning as Search in a Critical Region (Paper)</title>
<description>@article{4:20,author={Marco Botta and Attilio Giordana and Lorenza Saitta and Michle Sebag}, Title={Relational Learning as Search in a Critical Region},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/page03a/page03a.pdf}}</description>
<link>https://academictorrents.com/download/dabb4d1d3f16a74959bc65e6afb90da5c5382d1a</link>
</item>
<item>
<title>The SHOGUN Machine Learning Toolbox (Paper)</title>
<description>@article{11:60,author={Sren Sonnenburg and Gunnar Rtsch and Sebastian Henschel and Christian Widmer and Jonas Behr and Alexander Zien and Fabio de Bona and Alexander Binder and Christian Gehl and Vojtch Franc}, Title={The SHOGUN Machine Learning Toolbox},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/sonnenburg10a/sonnenburg10a.pdf}}</description>
<link>https://academictorrents.com/download/e210ec5a3ea46b557b4c798321e8712ba6f777e9</link>
</item>
<item>
<title>Regularization Techniques for Learning with Matrices (Paper)</title>
<description>@article{13:59,author={Sham M. Kakade and Shai Shalev-Shwartz and Ambuj Tewari}, Title={Regularization Techniques for Learning with Matrices},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/kakade12a/kakade12a.pdf}}</description>
<link>https://academictorrents.com/download/9886b06e76eca2473eed4df2e7370715c9e2ce13</link>
</item>
<item>
<title>LIBLINEAR: A Library for Large Linear Classification(Machine Learning Open Source Software Paper) (Paper)</title>
<description>@article{9:63,author={Eric Bax}, Title={LIBLINEAR: A Library for Large Linear Classification(Machine Learning Open Source Software Paper)},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/szlam08a/szlam08a.pdf}}</description>
<link>https://academictorrents.com/download/e20417f5f84f61a5aa948854fb3ca0e833116462</link>
</item>
<item>
<title>Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity (Paper)</title>
<description>@article{4:29,author={Shie Mannor and Ron Meir and Tong Zhang}, Title={Greedy Algorithms for Classification -- Consistency, Convergence Rates, and Adaptivity},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/clarke03a/clarke03a.pdf}}</description>
<link>https://academictorrents.com/download/a29d48ae0fca43488819fa4e74165a7b69f54536</link>
</item>
<item>
<title>Gaussian Processes for Machine Learning (GPML) Toolbox (Paper)</title>
<description>@article{11:100,author={Carl Edward Rasmussen and Hannes Nickisch}, Title={Gaussian Processes for Machine Learning (GPML) Toolbox},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/rasmussen10a/rasmussen10a.pdf}}</description>
<link>https://academictorrents.com/download/1574823977be15943c4505c7e51c3b1482f12051</link>
</item>
<item>
<title>Discriminative Hierarchical Part-based Models for Human Parsing and Action Recognition (Paper)</title>
<description>@article{13:99,author={Yang Wang and Duan Tran and Zicheng Liao and David Forsyth}, Title={Discriminative Hierarchical Part-based Models for Human Parsing and Action Recognition},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/wang12a/wang12a.pdf}}</description>
<link>https://academictorrents.com/download/c1ce30cc08e90c43ce7971ff45269699bc8fbfe8</link>
</item>
<item>
<title>Kronecker Graphs: An Approach to Modeling Networks (Paper)</title>
<description>@article{11:33,author={Jure Leskovec and Deepayan Chakrabarti and Jon Kleinberg and Christos Faloutsos and Zoubin Ghahramani}, Title={Kronecker Graphs: An Approach to Modeling Networks},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/leskovec10a/leskovec10a.pdf}}</description>
<link>https://academictorrents.com/download/faf09d55b358bb0af3e792b866a9225023afd00a</link>
</item>
<item>
<title>Integrating a Partial Model into Model Free Reinforcement Learning (Paper)</title>
<description>@article{13:61,author={Aviv Tamar and Dotan Di Castro and Ron Meir}, Title={Integrating a Partial Model into Model Free Reinforcement Learning},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/tamar12a/tamar12a.pdf}}</description>
<link>https://academictorrents.com/download/a130a36bb43f6138f9d10200d127650dd97d954b</link>
</item>
<item>
<title>A Streaming Parallel Decision Tree Algorithm (Paper)</title>
<description>@article{11:28,author={Yael Ben-Haim and Elad Tom-Tov}, Title={A Streaming Parallel Decision Tree Algorithm},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/ben-haim10a/ben-haim10a.pdf}}</description>
<link>https://academictorrents.com/download/6c9fde0154c251f556e2c6f0a674a0105b2f7c74</link>
</item>
<item>
<title>The Entire Regularization Path for the Support Vector Machine (Paper)</title>
<description>@article{5:50,author={Trevor Hastie and Saharon Rosset and Robert Tibshirani and Ji Zhu}, Title={The Entire Regularization Path for the Support Vector Machine},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/devito04a/devito04a.pdf}}</description>
<link>https://academictorrents.com/download/604e5a5844751bf804cb2b146eb4c6294efa1bd3</link>
</item>
<item>
<title>Forecasting Web Page Views: Methods and Observations (Paper)</title>
<description>@article{9:75,author={Abraham George and Warren B. Powell and Sanjeev R. Kulkarni}, Title={Forecasting Web Page Views: Methods and Observations},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/nickisch08a/nickisch08a.pdf}}</description>
<link>https://academictorrents.com/download/1f25f4cf7b6c1c3a8284e0b9b3e78b996af9df5c</link>
</item>
<item>
<title>Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes (Paper)</title>
<description>@article{11:106,author={Antti Honkela and Tapani Raiko and Mikael Kuusela and Matti Tornio and Juha Karhunen}, Title={Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/honkela10a/honkela10a.pdf}}</description>
<link>https://academictorrents.com/download/588ba0985c4ccb4026bf3f460d8831468842bd62</link>
</item>
<item>
<title>On Learning with Integral Operators (Paper)</title>
<description>@article{11:30,author={Lorenzo Rosasco and Mikhail Belkin and Ernesto De Vito}, Title={On Learning with Integral Operators},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/rosasco10a/rosasco10a.pdf}}</description>
<link>https://academictorrents.com/download/36cfb59ec3fe0fccd5171f44b1c1a93a40941001</link>
</item>
<item>
<title>Multi Kernel Learning with Online-Batch Optimization (Paper)</title>
<description>@article{13:8,author={Francesco Orabona and Luo Jie and Barbara Caputo}, Title={Multi Kernel Learning with Online-Batch Optimization},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/orabona12a/orabona12a.pdf}}</description>
<link>https://academictorrents.com/download/f2c29885d2175c129188959ca2dcc24e11e09bbd</link>
</item>
<item>
<title>Error-Correcting Output Codes Library (Paper)</title>
<description>@article{11:20,author={Sergio Escalera and Oriol Pujol and Petia Radeva}, Title={Error-Correcting Output Codes Library},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/escalera10a/escalera10a.pdf}}</description>
<link>https://academictorrents.com/download/85dbb42e94f90e3e0f9387458f384205a788839a</link>
</item>
<item>
<title>Near-optimal Regret Bounds for Reinforcement Learning (Paper)</title>
<description>@article{11:51,author={Thomas Jaksch and Ronald Ortner and Peter Auer}, Title={Near-optimal Regret Bounds for Reinforcement Learning},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/jaksch10a/jaksch10a.pdf}}</description>
<link>https://academictorrents.com/download/4f529518067fdcaebcd7a132ca84a640d1571b8a</link>
</item>
<item>
<title>Mean Field Variational Approximation for Continuous-Time Bayesian Networks (Paper)</title>
<description>@article{11:93,author={Ido Cohn and Tal El-Hay and Nir Friedman and Raz Kupferman}, Title={Mean Field Variational Approximation for Continuous-Time Bayesian Networks},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/cohn10a/cohn10a.pdf}}</description>
<link>https://academictorrents.com/download/38d6a74592f397d2b774b947d8e01630fab544d8</link>
</item>
<item>
<title>Learning over Sets using Kernel Principal Angles (Kernel Machines Section) (Paper)</title>
<description>@article{4:38,author={Lior Wolf and Amnon Shashua}, Title={Learning over Sets using Kernel Principal Angles (Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/mcallester03a/mcallester03a.pdf}}</description>
<link>https://academictorrents.com/download/fa48547a07c7bd76260da274c8502ce2a8e2ce0b</link>
</item>
<item>
<title>Image Categorization by Learning and Reasoning with Regions (Paper)</title>
<description>@article{5:33,author={ Yixin Chen and James Z. Wang}, Title={Image Categorization by Learning and Reasoning with Regions},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/schmitt04a/schmitt04a.pdf}}</description>
<link>https://academictorrents.com/download/fb6a9936f3cf21537b29a8a99dea5c3877ee6d91</link>
</item>
<item>
<title>Policy Search using Paired Comparisons (Paper)</title>
<description>@article{3:36,author={Malcolm J. A. Strens and Andrew W. Moore}, Title={Policy Search using Paired Comparisons},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/sebban02a/sebban02a.pdf}}</description>
<link>https://academictorrents.com/download/0492ad5e1760d75603445e1cbf927c68ee038059</link>
</item>
<item>
<title>Optimal Distributed Online Prediction Using Mini-Batches (Paper)</title>
<description>@article{13:6,author={Ofer Dekel and Ran Gilad-Bachrach and Ohad Shamir and Lin Xiao}, Title={Optimal Distributed Online Prediction Using Mini-Batches},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/dekel12a/dekel12a.pdf}}</description>
<link>https://academictorrents.com/download/3d011e776f9208473622f1274fc4185b92b2186d</link>
</item>
<item>
<title>An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data (Paper)</title>
<description>@article{11:6,author={Yufeng Ding and Jeffrey S. Simonoff}, Title={An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/ding10a/ding10a.pdf}}</description>
<link>https://academictorrents.com/download/c97acdd2dd26a94a8852d5869affca4b205de689</link>
</item>
<item>
<title>Consistency of Random Forests and Other Averaging Classifiers (Paper)</title>
<description>@article{9:68,author={Rong-En Fan and Kai-Wei Chang and Cho-Jui Hsieh and Xiang-Rui Wang and Chih-Jen Lin}, Title={Consistency of Random Forests and Other Averaging Classifiers},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/becerra-bonache08a/becerra-bonache08a.pdf}}</description>
<link>https://academictorrents.com/download/9261b13dd1a091906c79f51af274e57650583679</link>
</item>
<item>
<title>Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics (Paper)</title>
<description>@article{13:11,author={Michael U. Gutmann and Aapo Hyvrinen}, Title={Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/gutmann12a/gutmann12a.pdf}}</description>
<link>https://academictorrents.com/download/7a225973c264012100cb17c5b36f645c3689436b</link>
</item>
<item>
<title>Dynamic Policy Programming (Paper)</title>
<description>@article{13:103,author={Mohammad Gheshlaghi Azar and Vicen Gmez and Hilbert J. Kappen}, Title={Dynamic Policy Programming},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/azar12a/azar12a.pdf}}</description>
<link>https://academictorrents.com/download/29bf1e4d0803773356b58ba5c6727b75eb2212d7</link>
</item>
<item>
<title>WEKAExperiences with a Java Open-Source Project (Paper)</title>
<description>@article{11:87,author={Remco R. Bouckaert and Eibe Frank and Mark A. Hall and Geoffrey Holmes and Bernhard Pfahringer and Peter Reutemann and Ian H. Witten}, Title={WEKAExperiences with a Java Open-Source Project},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/bouckaert10a/bouckaert10a.pdf}}</description>
<link>https://academictorrents.com/download/435721f87df3273b5fd212ba43664f28946844ca</link>
</item>
<item>
<title>Beyond Independent Components: Trees and Clusters (Paper)</title>
<description>@article{4:48,author={Francis R. Bach and Michael I. Jordan}, Title={Beyond Independent Components: Trees and Clusters},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/cardoso03a/cardoso03a.pdf}}</description>
<link>https://academictorrents.com/download/e377facbfe5a4a2fb8a80cd62fe9bb6b0c64b7ed</link>
</item>
<item>
<title>How to Explain Individual Classification Decisions (Paper)</title>
<description>@article{11:61,author={David Baehrens and Timon Schroeter and Stefan Harmeling and Motoaki Kawanabe and Katja Hansen and Klaus-Robert Mller}, Title={How to Explain Individual Classification Decisions},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/baehrens10a/baehrens10a.pdf}}</description>
<link>https://academictorrents.com/download/783985a34ad124c9f34c3cdc1c9e1e44daf3daf3</link>
</item>
<item>
<title>Training and Testing Low-degree Polynomial Data Mappings via Linear SVM (Paper)</title>
<description>@article{11:48,author={Yin-Wen Chang and Cho-Jui Hsieh and Kai-Wei Chang and Michael Ringgaard and Chih-Jen Lin}, Title={Training and Testing Low-degree Polynomial Data Mappings via Linear SVM},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/chang10a/chang10a.pdf}}</description>
<link>https://academictorrents.com/download/4eae73f4109e57ed37f44dd42abca3994cbbc709</link>
</item>
<item>
<title>Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation (Paper)</title>
<description>@article{11:42,author={Vicen Gmez and Hilbert J. Kappen and Michael Chertkov}, Title={Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/gomez10a/gomez10a.pdf}}</description>
<link>https://academictorrents.com/download/2f4baef4b5a92f3bf9b9b0ee8cdbdf350734eb53</link>
</item>
<item>
<title>Maximum Relative Margin and Data-Dependent Regularization (Paper)</title>
<description>@article{11:25,author={Pannagadatta K. Shivaswamy and Tony Jebara}, Title={Maximum Relative Margin and Data-Dependent Regularization},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/shivaswamy10a/shivaswamy10a.pdf}}</description>
<link>https://academictorrents.com/download/db869ba2cbad69ec5a64e02097e4883caf786755</link>
</item>
<item>
<title>Tree Decomposition for Large-Scale SVM Problems (Paper)</title>
<description>@article{11:98,author={Fu Chang and Chien-Yang Guo and Xiao-Rong Lin and Chi-Jen Lu}, Title={Tree Decomposition for Large-Scale SVM Problems},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/chang10b/chang10b.pdf}}</description>
<link>https://academictorrents.com/download/7fb05f96b721e34657763f2902d2acd2063393f9</link>
</item>
<item>
<title>Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure (Paper)</title>
<description>@article{11:9,author={Kaname Kojima and Eric Perrier and Seiya Imoto and Satoru Miyano}, Title={Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/kojima10a/kojima10a.pdf}}</description>
<link>https://academictorrents.com/download/4ee1ee4aad1d7f31e33468aa7c9d7ce642ebfe33</link>
</item>
<item>
<title>Blind Source Separation via Generalized Eigenvalue Decomposition (Paper)</title>
<description>@article{4:50,author={Lucas Parra and Paul Sajda}, Title={Blind Source Separation via Generalized Eigenvalue Decomposition},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/teh03a/teh03a.pdf}}</description>
<link>https://academictorrents.com/download/807ec78c5f243c58f7ecaefe9024be34ce1743d4</link>
</item>
<item>
<title>Learning Translation Invariant Kernels for Classification (Paper)</title>
<description>@article{11:45,author={Kamaledin Ghiasi-Shirazi and Reza Safabakhsh and Mostafa Shamsi}, Title={Learning Translation Invariant Kernels for Classification},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/ghiasi-shirazi10a/ghiasi-shirazi10a.pdf}}</description>
<link>https://academictorrents.com/download/777c5ed841f28d051cfb71b47ab4a8172c7cebc5</link>
</item>
<item>
<title>Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds (Paper)</title>
<description>@article{11:108,author={Jacek P. Dmochowski and Paul Sajda and Lucas C. Parra}, Title={Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/dmochowski10a/dmochowski10a.pdf}}</description>
<link>https://academictorrents.com/download/b2f002d5c1b1d7b170c7f4932d7afbb8e8d98793</link>
</item>
<item>
<title>Hilbert Space Embeddings and Metrics on Probability Measures (Paper)</title>
<description>@article{11:50,author={Bharath K. Sriperumbudur and Arthur Gretton and Kenji Fukumizu and Bernhard Schlkopf and Gert R.G. Lanckriet}, Title={Hilbert Space Embeddings and Metrics on Probability Measures},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/sriperumbudur10a/sriperumbudur10a.pdf}}</description>
<link>https://academictorrents.com/download/df6ff4362640658f647c606031aa9abda69c079d</link>
</item>
<item>
<title>Tree-Structured Neural Decoding (Paper)</title>
<description>@article{4:30,author={Christian d'Avignon and Donald Geman}, Title={Tree-Structured Neural Decoding},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/mannor03a/mannor03a.pdf}}</description>
<link>https://academictorrents.com/download/ee445f4745a448c0f32fd1531215ce690d3b1867</link>
</item>
<item>
<title>Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling (Paper)</title>
<description>@article{3:33,author={Tobias Scheffer and Stefan Wrobel}, Title={Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/nair02a/nair02a.pdf}}</description>
<link>https://academictorrents.com/download/569ee0e53edf4e9564861d89d17f052d9dba6087</link>
</item>
<item>
<title>Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing (Paper)</title>
<description>@article{11:59,author={Ryo Yoshida and Mike West}, Title={Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/yoshida10a/yoshida10a.pdf}}</description>
<link>https://academictorrents.com/download/7a7ddb4f7ad47182442b2bdd47215e3631970c9c</link>
</item>
<item>
<title>Practical Approaches to Principal Component Analysis in the Presence of Missing Values (Paper)</title>
<description>@article{11:66,author={Alexander Ilin and Tapani Raiko}, Title={Practical Approaches to Principal Component Analysis in the Presence of Missing Values},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/ilin10a/ilin10a.pdf}}</description>
<link>https://academictorrents.com/download/761fc1338d7f2a89e8547f28208e3ebdfc5c11c2</link>
</item>
<item>
<title>Second-Order Bilinear Discriminant Analysis (Paper)</title>
<description>@article{11:21,author={Christoforos Christoforou and Robert Haralick and Paul Sajda and Lucas C. Parra}, Title={Second-Order Bilinear Discriminant Analysis},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/christoforou10a/christoforou10a.pdf}}</description>
<link>https://academictorrents.com/download/ac52deadf849bcedf323fd472d3c0051caa8c7d4</link>
</item>
<item>
<title>Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies (Paper)</title>
<description>@article{13:39,author={Ioannis Tsamardinos and Sofia Triantafillou and Vincenzo Lagani}, Title={Towards Integrative Causal Analysis of Heterogeneous Data Sets and Studies},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/tsamardinos12a/tsamardinos12a.pdf}}</description>
<link>https://academictorrents.com/download/9ef8e09e06220181e0521d4e4cec1db8ed713233</link>
</item>
<item>
<title>Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary -Mixing Processes (Paper)</title>
<description>@article{11:65,author={Liva Ralaivola and Marie Szafranski and Guillaume Stempfel}, Title={Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary -Mixing Processes},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/ralaivola10a/ralaivola10a.pdf}}</description>
<link>https://academictorrents.com/download/2d42e44af64e4da873db06498a34264f0ca4c5f4</link>
</item>
<item>
<title>Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models (Paper)</title>
<description>@article{9:31,author={Sungwook Yoon and Alan Fern and Robert Givan}, Title={Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/chen08a/chen08a.pdf}}</description>
<link>https://academictorrents.com/download/d7a91c029fffd0bcd5aca304fcdfb635d51449df</link>
</item>
<item>
<title>MULTIBOOST: A Multi-purpose Boosting Package (Paper)</title>
<description>@article{13:18,author={Djalel Benbouzid and Rbert Busa-Fekete and Norman Casagrande and Franois-David Collin and Balzs Kgl}, Title={MULTIBOOST: A Multi-purpose Boosting Package},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/benbouzid12a/benbouzid12a.pdf}}</description>
<link>https://academictorrents.com/download/43ba4aab8e4826a7a678aa7656546074a8fe84e0</link>
</item>
<item>
<title>Introduction to the Special Issue on Learning Theory (Paper)</title>
<description>@article{4:31,author={Ralf Herbrich and Thore Graepel}, Title={Introduction to the Special Issue on Learning Theory},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/davignon03a/davignon03a.pdf}}</description>
<link>https://academictorrents.com/download/26f224552202d280661bf7ddd130bdb9af86c005</link>
</item>
<item>
<title>An Empirical Study of the Use of Relevance Information in Inductive Logic Programming (Paper)</title>
<description>@article{4:16,author={Ashwin Srinivasan and Ross D. King and Michael E. Bain}, Title={An Empirical Study of the Use of Relevance Information in Inductive Logic Programming},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/srinivasan03a/srinivasan03a.pdf}}</description>
<link>https://academictorrents.com/download/9ca794dc4c0c8a6bfce301172ab3aa72b499bd1c</link>
</item>
<item>
<title>Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes (Paper)</title>
<description>@article{11:34,author={Pradeep Ravikumar and Alekh Agarwal and Martin J. Wainwright}, Title={Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/ravikumar10a/ravikumar10a.pdf}}</description>
<link>https://academictorrents.com/download/781510d86759f5991a9a465120e26f4ef5088f6d</link>
</item>
<item>
<title>Bounding the Probability of Error for High Precision Optical Character Recognition (Paper)</title>
<description>@article{13:12,author={Gary B. Huang and Andrew Kae and Carl Doersch and Erik Learned-Miller}, Title={Bounding the Probability of Error for High Precision Optical Character Recognition},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/huang12a/huang12a.pdf}}</description>
<link>https://academictorrents.com/download/412368484d2ada74fa61a9671199847e1b8156f3</link>
</item>
<item>
<title>Online Learning for Matrix Factorization and Sparse Coding (Paper)</title>
<description>@article{11:2,author={Julien Mairal and Francis Bach and Jean Ponce and Guillermo Sapiro}, Title={Online Learning for Matrix Factorization and Sparse Coding},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/mairal10a/mairal10a.pdf}}</description>
<link>https://academictorrents.com/download/54687040e505d30dc4b9c3243d634089c21ca8f2</link>
</item>
<item>
<title>Learning Linear Cyclic Causal Models with Latent Variables (Paper)</title>
<description>@article{13:109,author={Antti Hyttinen and Frederick Eberhardt and Patrik O. Hoyer}, Title={Learning Linear Cyclic Causal Models with Latent Variables},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/hyttinen12a/hyttinen12a.pdf}}</description>
<link>https://academictorrents.com/download/dd421e0587936b94743880c4ffd81401dbb6c372</link>
</item>
<item>
<title>Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models (Paper)</title>
<description>@article{11:27,author={Fang-Lan Huang and Cho-Jui Hsieh and Kai-Wei Chang and Chih-Jen Lin}, Title={Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/huang10a/huang10a.pdf}}</description>
<link>https://academictorrents.com/download/f6ebbdc8e0fb50dae3718ca954f52ccb7adb4047</link>
</item>
<item>
<title>Classification with a Reject Option using a Hinge Loss (Paper)</title>
<description>@article{9:61,author={Balzs Csand Csji and Lszl Monostori}, Title={Classification with a Reject Option using a Hinge Loss},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/dalalyan08a/dalalyan08a.pdf}}</description>
<link>https://academictorrents.com/download/0db1513fac174b28bdb073ea542a430e799bc9ed</link>
</item>
<item>
<title>Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components (Paper)</title>
<description>@article{3:5,author={Kwokleung Chan and Te-Won Lee and Terrence J. Sejnowski}, Title={Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/chan02a/chan02a.pdf}}</description>
<link>https://academictorrents.com/download/3a3601d50ea073a2b63cd09c243c4854db5f369e</link>
</item>
<item>
<title>ICA Using Spacings Estimates of Entropy (Paper)</title>
<description>@article{4:51,author={Erik G. Learned-Miller and John W. Fisher III}, Title={ICA Using Spacings Estimates of Entropy},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/parra03a/parra03a.pdf}}</description>
<link>https://academictorrents.com/download/2f3b72e0452c5e062c9ec69c6a9a4c89c6e454a3</link>
</item>
<item>
<title>A Fast Hybrid Algorithm for Large-Scale l1-Regularized Logistic Regression (Paper)</title>
<description>@article{11:23,author={Jianing Shi and Wotao Yin and Stanley Osher and Paul Sajda}, Title={A Fast Hybrid Algorithm for Large-Scale l1-Regularized Logistic Regression},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/shi10a/shi10a.pdf}}</description>
<link>https://academictorrents.com/download/992fc5a3b795a31d6b455b1d62bc010e7a643304</link>
</item>
<item>
<title>Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces (Paper)</title>
<description>@article{5:3,author={Kenji Fukumizu and Francis R. Bach and Michael I. Jordan}, Title={Dimensionality Reduction for Supervised Learning with Reproducing Kernel
  Hilbert Spaces},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/fukumizu04a/fukumizu04a.pdf}}</description>
<link>https://academictorrents.com/download/74cb0646ae7131abd9a5a74185769d15f0c12959</link>
</item>
<item>
<title>Matching Words and Pictures (Paper)</title>
<description>@article{3:43,author={Kobus Barnard and Pinar Duygulu and David Forsyth and Nando de Freitas,David M. Blei and Michael I. Jordan}, Title={Matching Words and Pictures},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/cancedda03a/cancedda03a.pdf}}</description>
<link>https://academictorrents.com/download/d30c702637eeb68a57def309ace329842069bf93</link>
</item>
<item>
<title>Robust Kernel Density Estimation (Paper)</title>
<description>@article{13:82,author={JooSeuk Kim and Clayton D. Scott}, Title={Robust Kernel Density Estimation},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/kim12b/kim12b.pdf}}</description>
<link>https://academictorrents.com/download/8f9e1d5c029a01bdc341af554109ac93a6cb2363</link>
</item>
<item>
<title>Efficient Algorithms for Conditional Independence Inference (Paper)</title>
<description>@article{11:112,author={Remco Bouckaert and Raymond Hemmecke and Silvia Lindner and Milan Studen}, Title={Efficient Algorithms for Conditional Independence Inference},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/bouckaert10b/bouckaert10b.pdf}}</description>
<link>https://academictorrents.com/download/7db911570613fe0e2aca854b562db381906a270a</link>
</item>
<item>
<title>Generalized Power Method for Sparse Principal Component Analysis (Paper)</title>
<description>@article{11:15,author={Michel Journe and Yurii Nesterov and Peter Richtrik and Rodolphe Sepulchre}, Title={Generalized Power Method for Sparse Principal Component Analysis},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/journee10a/journee10a.pdf}}</description>
<link>https://academictorrents.com/download/9f3b021d777a45db7a13f44e8bea155b8cb58085</link>
</item>
<item>
<title>Classification with Incomplete Data Using Dirichlet Process Priors (Paper)</title>
<description>@article{11:107,author={Chunping Wang and Xuejun Liao and Lawrence Carin and David B. Dunson}, Title={Classification with Incomplete Data Using Dirichlet Process Priors},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/wang10a/wang10a.pdf}}</description>
<link>https://academictorrents.com/download/afb17be0c8af025e713c385fa5ed3d66a4b06c9b</link>
</item>
<item>
<title>Manifold Learning: The Price of Normalization (Paper)</title>
<description>@article{9:65,author={Michael Collins and Amir Globerson and Terry Koo and Xavier Carreras and Peter L. Bartlett}, Title={Manifold Learning: The Price of Normalization},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/crammer08a/crammer08a.pdf}}</description>
<link>https://academictorrents.com/download/b32416de370acf8ec5131d8f4099675600e114c4</link>
</item>
<item>
<title>Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (Paper)</title>
<description>@article{11:7,author={Constantin F. Aliferis and Alexander Statnikov and Ioannis Tsamardinos and Subramani Mani and Xenofon D. Koutsoukos}, Title={Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/aliferis10a/aliferis10a.pdf}}</description>
<link>https://academictorrents.com/download/23d65f7e7c185f595fb4c850f448f6cea7668c28</link>
</item>
<item>
<title>Spectral Regularization Algorithms for Learning Large Incomplete Matrices (Paper)</title>
<description>@article{11:80,author={Rahul Mazumder and Trevor Hastie and Robert Tibshirani}, Title={Spectral Regularization Algorithms for Learning Large Incomplete Matrices},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/mazumder10a/mazumder10a.pdf}}</description>
<link>https://academictorrents.com/download/4fcb51e85df93faa5dd2c8496117c427eaafae8a</link>
</item>
<item>
<title>Weather Data Mining Using Independent Component Analysis (Paper)</title>
<description>@article{5:9,author={Jayanta Basak and Anant Sudarshan and Deepak Trivedi and M. S.
  Santhanam}, Title={Weather Data Mining Using Independent Component Analysis},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/mendelson04a/mendelson04a.pdf}}</description>
<link>https://academictorrents.com/download/4ff6ef8523a58c14beafa5e99f512e96bc2f6b10</link>
</item>
<item>
<title>Large Scale Online Learning of Image Similarity Through Ranking (Paper)</title>
<description>@article{11:36,author={Gal Chechik and Varun Sharma and Uri Shalit and Samy Bengio}, Title={Large Scale Online Learning of Image Similarity Through Ranking},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/chechik10a/chechik10a.pdf}}</description>
<link>https://academictorrents.com/download/c95e5c45a8cfa5b3b07d7bcce803e7fc5d4c5284</link>
</item>
<item>
<title>Incremental Sigmoid Belief Networks for Grammar Learning (Paper)</title>
<description>@article{11:115,author={James Henderson and Ivan Titov}, Title={Incremental Sigmoid Belief Networks for Grammar Learning},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/henderson10a/henderson10a.pdf}}</description>
<link>https://academictorrents.com/download/0b615aa4e85be044021050e8abf2834a1da2fe28</link>
</item>
<item>
<title>Active Learning of Causal Networks with Intervention Experiments and Optimal Designs(Special Topic on Causality) (Paper)</title>
<description>@article{9:86,author={Guy Lebanon and Yi Mao}, Title={Active Learning of Causal Networks with Intervention Experiments and Optimal Designs(Special Topic on Causality)},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/debruyne08a/debruyne08a.pdf}}</description>
<link>https://academictorrents.com/download/518e14455aa670fa96c89c2fa86421a5e19011a6</link>
</item>
<item>
<title>Matrix Completion from Noisy Entries (Paper)</title>
<description>@article{11:69,author={Raghunandan H. Keshavan and Andrea Montanari and Sewoong Oh}, Title={Matrix Completion from  Noisy Entries},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/keshavan10a/keshavan10a.pdf}}</description>
<link>https://academictorrents.com/download/0756c4a008924839bdbb8c0d73113dfa6c636f9d</link>
</item>
<item>
<title>A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design (Paper)</title>
<description>@article{11:68,author={Dirk Gorissen and Ivo Couckuyt and Piet Demeester and Tom Dhaene and Karel Crombecq}, Title={A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/gorissen10a/gorissen10a.pdf}}</description>
<link>https://academictorrents.com/download/a0c9ac85b5d5a386250be4451913a03020e78931</link>
</item>
<item>
<title>Distance-Based Classification with Lipschitz Functions (Special Topic on Learning Theory) (Paper)</title>
<description>@article{5:25,author={Ulrike von Luxburg and Olivier Bousquet}, Title={Distance-Based Classification with Lipschitz Functions (Special Topic on Learning Theory)},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/blum04a/blum04a.pdf}}</description>
<link>https://academictorrents.com/download/e66e7c65690698c34045fb6a89085f020a9d5fcf</link>
</item>
<item>
<title>Combining Knowledge from Different Sources in Causal Probabilistic Models (Paper)</title>
<description>@article{4:13,author={Marek J. Druzdzel and Francisco J. Dez}, Title={Combining Knowledge from Different Sources in Causal Probabilistic Models},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/druzdzel03a/druzdzel03a.pdf}}</description>
<link>https://academictorrents.com/download/3b8ce43d2b727fb96fde0ca485c41589d67bc11e</link>
</item>
<item>
<title>Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds (Paper)</title>
<description>@article{4:7,author={Lawrence K. Saul and Sam T. Roweis}, Title={Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/saul03a/saul03a.pdf}}</description>
<link>https://academictorrents.com/download/1b5ba31cd3df0adfc9aaadfac2d4ef13fe6ba423</link>
</item>
<item>
<title>Quadratic Programming Feature Selection (Paper)</title>
<description>@article{11:49,author={Irene Rodriguez-Lujan and Ramon Huerta and Charles Elkan and Carlos Santa Cruz}, Title={Quadratic Programming Feature Selection},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/rodriguez-lujan10a/rodriguez-lujan10a.pdf}}</description>
<link>https://academictorrents.com/download/109e4b0af9eda299d253009b35086107a18f7c3d</link>
</item>
<item>
<title>Efficient Feature Selection via Analysis of Relevance and Redundancy (Paper)</title>
<description>@article{5:44,author={Lei Yu and Huan Liu}, Title={Efficient Feature Selection via Analysis of Relevance and Redundancy},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/dash04a/dash04a.pdf}}</description>
<link>https://academictorrents.com/download/143139de95c2900b81e43253332964e43579eccb</link>
</item>
<item>
<title>On the Proper Learning of Axis-Parallel Concepts (Paper)</title>
<description>@article{4:8,author={Nader H. Bshouty and Lynn Burroughs}, Title={On the Proper Learning of Axis-Parallel Concepts},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/bshouty03a/bshouty03a.pdf}}</description>
<link>https://academictorrents.com/download/9e1cbfb7707e416e8678dbddcd2b8621144e3d8d</link>
</item>
<item>
<title>Distance Metric Learning with Eigenvalue Optimization (Paper)</title>
<description>@article{13:1,author={Yiming Ying and Peng Li}, Title={Distance Metric Learning with Eigenvalue Optimization},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/ying12a/ying12a.pdf}}</description>
<link>https://academictorrents.com/download/82d7811db75a699d123540f97f5a10ecf82568b0</link>
</item>
<item>
<title>Kernel Independent Component Analysis (Kernel Machines Section) (Paper)</title>
<description>@article{3:1,author={Francis R. Bach and Michael I. Jordan}, Title={Kernel Independent Component Analysis
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/bach02a/bach02a.pdf}}</description>
<link>https://academictorrents.com/download/9aff3a0db5031f9e10b8262bea3ae30e11d2af3d</link>
</item>
<item>
<title>Consistency of Trace Norm Minimization (Paper)</title>
<description>@article{9:37,author={Andreas Christmann and Arnout Van Messem}, Title={Consistency of Trace Norm Minimization},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/drton08a/drton08a.pdf}}</description>
<link>https://academictorrents.com/download/23483f229c9ced5b0e0fe4cf631d06ee881a67d9</link>
</item>
<item>
<title>Mal-ID: Automatic Malware Detection Using Common Segment Analysis and Meta-Features (Paper)</title>
<description>@article{13:33,author={Gil Tahan and Lior Rokach and Yuval Shahar}, Title={Mal-ID: Automatic Malware Detection Using Common Segment Analysis and Meta-Features},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/tahan12a/tahan12a.pdf}}</description>
<link>https://academictorrents.com/download/6c1daa1fab43805daeddd2c3cb77ab6e2499be05</link>
</item>
<item>
<title>A Multi-Stage Framework for Dantzig Selector and LASSO (Paper)</title>
<description>@article{13:41,author={Ji Liu and Peter Wonka and Jieping Ye}, Title={A Multi-Stage Framework for Dantzig Selector and LASSO},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/liu12a/liu12a.pdf}}</description>
<link>https://academictorrents.com/download/889cda18e25e3f41a8b9252e5449eeda18548b66</link>
</item>
<item>
<title>Stochastic Composite Likelihood (Paper)</title>
<description>@article{11:89,author={Joshua V. Dillon and Guy Lebanon}, Title={Stochastic Composite Likelihood},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/dillon10a/dillon10a.pdf}}</description>
<link>https://academictorrents.com/download/78716f1d436c9cbc4137a8e4552c596bec1256d7</link>
</item>
<item>
<title>The Minimum Error Minimax Probability Machine (Paper)</title>
<description>@article{5:46,author={Kaizhu Huang and Haiqin Yang and Irwin King and Michael R. Lyu and Laiwan Chan}, Title={The Minimum Error Minimax Probability Machine},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/zhang04b/zhang04b.pdf}}</description>
<link>https://academictorrents.com/download/6ef48588ccadd8a66f4fb57556644b46988251ce</link>
</item>
<item>
<title>An Efficient Boosting Algorithm for Combining Preferences (Paper)</title>
<description>@article{4:39,author={Yoav Freund and Raj Iyer and Robert E. Schapire and Yoram Singer}, Title={An Efficient Boosting Algorithm for Combining Preferences},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/wolf03a/wolf03a.pdf}}</description>
<link>https://academictorrents.com/download/776004e8c9e95787577b92fc7d95bd36619ad351</link>
</item>
<item>
<title>Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence (Paper)</title>
<description>@article{11:75,author={Qiang Wu and Justin Guinney and Mauro Maggioni and Sayan Mukherjee}, Title={Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence},journal={Journal of Machine Learning Research},volume={11}, url={http://www.jmlr.org/papers/volume11/wu10a/wu10a.pdf}}</description>
<link>https://academictorrents.com/download/5f6cc184ae73ffa7eb7e30ebdf5e9f3698c4838a</link>
</item>
<item>
<title>Sources of Success for Boosted Wrapper Induction (Paper)</title>
<description>@article{5:18,author={David Kauchak and Joseph Smarr and Charles Elkan}, Title={Sources of Success for Boosted Wrapper Induction},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/clark04a/clark04a.pdf}}</description>
<link>https://academictorrents.com/download/2e675fd95fd66b8e1ec4484f32b4ac8ea0233257</link>
</item>
<item>
<title>PAC-learnability of Probabilistic Deterministic Finite State Automata (Paper)</title>
<description>@article{5:17,author={Alexander Clark and Franck Thollard}, Title={PAC-learnability of Probabilistic Deterministic Finite State Automata},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/higuchi04a/higuchi04a.pdf}}</description>
<link>https://academictorrents.com/download/df1998a71077eb7a760183a3113069ca656304b8</link>
</item>
<item>
<title>Multi-task Regression using Minimal Penalties (Paper)</title>
<description>@article{13:90,author={Matthieu Solnon and Sylvain Arlot and Francis Bach}, Title={Multi-task Regression using Minimal Penalties},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/solnon12a/solnon12a.pdf}}</description>
<link>https://academictorrents.com/download/26f1d4b3dd6d99ab4b66c15207ba1c667d83abe1</link>
</item>
<item>
<title>Feature Discovery in Non-Metric Pairwise Data (Paper)</title>
<description>@article{5:29,author={Julian Laub and Klaus-Robert Mller}, Title={Feature Discovery in Non-Metric Pairwise Data},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/ziehe04a/ziehe04a.pdf}}</description>
<link>https://academictorrents.com/download/fad3010c2d13bf634f90b8c452e96742d6d6bcc7</link>
</item>
<item>
<title>An Approximate Analytical Approach to Resampling Averages (Kernel Machines Section) (Paper)</title>
<description>@article{4:45,author={Drthe Malzahn and Manfred Opper}, Title={An Approximate Analytical Approach to Resampling Averages (Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/lagoudakis03a/lagoudakis03a.pdf}}</description>
<link>https://academictorrents.com/download/0460c8c1896c195a47791759abe21686cbb59962</link>
</item>
<item>
<title>On the Convergence Rate of lp-Norm Multiple Kernel Learning (Paper)</title>
<description>@article{13:80,author={Marius Kloft and Gilles Blanchard}, Title={On the Convergence Rate of lp-Norm Multiple Kernel Learning},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/kloft12a/kloft12a.pdf}}</description>
<link>https://academictorrents.com/download/5cf922c6e8277a4e554a5370c8066469c238cd48</link>
</item>
<item>
<title>SVMTorch: Support Vector Machines for Large-Scale Regression Problems (Kernel Machines Section) (Paper)</title>
<description>@article{1:5,author={Ronan Collobert and Samy Bengio}, Title={SVMTorch: Support Vector Machines for Large-Scale Regression Problems
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={1}, url={http://www.jmlr.org/papers/volume1/collobert01a/collobert01a.pdf}}</description>
<link>https://academictorrents.com/download/e56fbff6604eb9544fd702e368479af155406413</link>
</item>
<item>
<title>A Unified Framework for Model-based Clustering (Paper)</title>
<description>@article{4:41,author={Shi Zhong and Joydeep Ghosh}, Title={A Unified Framework for Model-based Clustering},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/hutter03a/hutter03a.pdf}}</description>
<link>https://academictorrents.com/download/818f8f9a9d516aeb11ca86f8df6d0a5a8febfd45</link>
</item>
<item>
<title>Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters (Paper)</title>
<description>@article{5:16,author={Isao Higuchi and Shinto Eguchi}, Title={Robust Principal Component Analysis with Adaptive Selection for Tuning
  Parameters},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/chawla04a/chawla04a.pdf}}</description>
<link>https://academictorrents.com/download/aaf5ad6fca5f36419ba219aa90bd87c800b18054</link>
</item>
<item>
<title>Inducing Grammars from Sparse Data Sets: A Survey of Algorithms and Results (Paper)</title>
<description>@article{4:25,author={Orlando Cicchello and Stefan C. Kremer}, Title={Inducing Grammars from Sparse Data Sets: A Survey of Algorithms and Results},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/baldi03a/baldi03a.pdf}}</description>
<link>https://academictorrents.com/download/c867c760e4256aafb009cc7e5b9d893430c4f6e4</link>
</item>
<item>
<title>Benefitting from the Variables that Variable Selection Discards (Paper)</title>
<description>@article{3:49,author={Rich Caruana and Virginia R. de Sa}, Title={Benefitting from the Variables that Variable Selection Discards},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/bengio03b/bengio03b.pdf}}</description>
<link>https://academictorrents.com/download/b388afb9c9ce20102130a3ecdddf4e685e49797d</link>
</item>
<item>
<title>Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Paper)</title>
<description>@article{13:79,author={Alain Hauser and Peter Bhlmann}, Title={Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/hauser12a/hauser12a.pdf}}</description>
<link>https://academictorrents.com/download/294776d4fe640bb3e5ad3dfcd2328f2346c3d0e0</link>
</item>
<item>
<title>On Inclusion-Driven Learning of Bayesian Networks (Paper)</title>
<description>@article{4:23,author={Robert Castelo and Toms Kocka}, Title={On Inclusion-Driven Learning of Bayesian Networks},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/claveau03a/claveau03a.pdf}}</description>
<link>https://academictorrents.com/download/fe662fc45e7629b07622f64444a77b710a5729bb</link>
</item>
<item>
<title>Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences (Paper)</title>
<description>@article{13:62,author={Jan Grau and Jens Keilwagen and Andr Gohr and Berit Haldemann and Stefan Posch and Ivo Grosse}, Title={Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/grau12a/grau12a.pdf}}</description>
<link>https://academictorrents.com/download/9ff938b61ed7b5e3d5ebfe3278ce56bd5cc5a3f2</link>
</item>
<item>
<title>Feature Selection for Unsupervised Learning (Paper)</title>
<description>@article{5:31,author={Jennifer G. Dy and Carla E. Brodley}, Title={Feature Selection for Unsupervised Learning},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/jebara04a/jebara04a.pdf}}</description>
<link>https://academictorrents.com/download/9fe1df0d4a7ded57d447c97318b35e60810e32a2</link>
</item>
<item>
<title>Overlearning in Marginal Distribution-Based ICA: Analysis and Solutions (Paper)</title>
<description>@article{4:58,author={Jaakko Srel&amp;auml and Ricardo Vigrio}, Title={Overlearning in Marginal Distribution-Based ICA: Analysis and Solutions},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/waheed03a/waheed03a.pdf}}</description>
<link>https://academictorrents.com/download/deb20678ef8a6bbb391b199cafc48998a39ffc1d</link>
</item>
<item>
<title>Learning the Kernel Matrix with Semidefinite Programming (Paper)</title>
<description>@article{5:2,author={Gert R.G. Lanckriet and Nello Cristianini and Peter Bartlett and Laurent El
  Ghaoui and Michael I. Jordan}, Title={Learning the Kernel Matrix with Semidefinite Programming},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/lanckriet04a/lanckriet04a.pdf}}</description>
<link>https://academictorrents.com/download/2d9247a0b27d6f1130bdcd8b869830ecd8c6243e</link>
</item>
<item>
<title>In Defense of One-Vs-All Classification (Paper)</title>
<description>@article{5:4,author={Ryan Rifkin and Aldebaro Klautau}, Title={In Defense of One-Vs-All Classification},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/fukumizu04a/fukumizu04a-erratum.pdf}}</description>
<link>https://academictorrents.com/download/39551e7bd5f0af1f567a2fef9413938c566f0d3b</link>
</item>
<item>
<title>Learning Ensembles from Bites: A Scalable and Accurate Approach (Paper)</title>
<description>@article{5:15,author={Nitesh V. Chawla and Lawrence O. Hall and Kevin W. Bowyer and W. Philip
  Kegelmeyer}, Title={Learning Ensembles from Bites: A Scalable and Accurate Approach},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/quist04a/quist04a.pdf}}</description>
<link>https://academictorrents.com/download/76fb996864ee7fd80e65c8f6fe44478bb12bbd71</link>
</item>
<item>
<title>MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling (Paper)</title>
<description>@article{3:56,author={Isabelle Rivals and Lon Personnaz}, Title={MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/perkins03a/perkins03a.pdf}}</description>
<link>https://academictorrents.com/download/2e520073db4ce011c5a05d5a8b08496d47865f9f</link>
</item>
<item>
<title>Fast String Kernels using Inexact Matching for Protein Sequences (Paper)</title>
<description>@article{5:52,author={Christina Leslie and Rui Kuang}, Title={Fast String Kernels using Inexact Matching for Protein Sequences},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/bhattacharyya04a/bhattacharyya04a.pdf}}</description>
<link>https://academictorrents.com/download/cd5521215c0dc806e8eba38813d34d8263c5ee3c</link>
</item>
<item>
<title>Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection (Paper)</title>
<description>@article{13:2,author={Gavin Brown and Adam Pocock and Ming-Jie Zhao and Mikel Lujn}, Title={Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/brown12a/brown12a.pdf}}</description>
<link>https://academictorrents.com/download/2c623a098b9f668b9501b3606ab5f94034d81396</link>
</item>
<item>
<title>Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction (Paper)</title>
<description>@article{4:9,author={Mary Elaine Califf and Raymond J. Mooney}, Title={Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction},journal={Journal of Machine Learning Research},volume={4}, url={http://www.jmlr.org/papers/volume4/califf03a/califf03a.pdf}}</description>
<link>https://academictorrents.com/download/923d25dc1171104ed2a862fbdfc218ff56f5aa68</link>
</item>
<item>
<title>Tracking the Best Linear Predictor (Paper)</title>
<description>@article{1:10,author={Mark Herbster and Manfred K. Warmuth}, Title={Tracking the Best Linear Predictor},journal={Journal of Machine Learning Research},volume={1}, url={http://www.jmlr.org/papers/volume1/herbster01a/herbster01a.pdf}}</description>
<link>https://academictorrents.com/download/c09e7c3fba6fcc802002d6f9a15fe54d8dee5a94</link>
</item>
<item>
<title>Linear Regression With Random Projections (Paper)</title>
<description>@article{13:89,author={Odalric-Ambrym Maillard and Rmi Munos}, Title={Linear Regression With Random Projections},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/maillard12a/maillard12a.pdf}}</description>
<link>https://academictorrents.com/download/eb003a400246f6e364bf3ac0bb44067055988bd4</link>
</item>
<item>
<title>The Sample Complexity of Exploration in the Multi-Armed Bandit Problem (Special Topic on Learning Theory) (Paper)</title>
<description>@article{5:23,author={Shie Mannor and John N. Tsitsiklis}, Title={The Sample Complexity of Exploration in the Multi-Armed Bandit Problem (Special Topic on Learning Theory)},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/ginter04a/ginter04a.pdf}}</description>
<link>https://academictorrents.com/download/1e3a97735443c7592be48b78733f2349ee0b403f</link>
</item>
<item>
<title>The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins (Paper)</title>
<description>@article{5:56,author={Cynthia Rudin and Ingrid Daubechies and Robert E. Schapire}, Title={The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/fleuret04a/fleuret04a.pdf}}</description>
<link>https://academictorrents.com/download/af7c1049149f10db2ade5a6b9d9b6fecdcecb0a1</link>
</item>
<item>
<title>Structured Sparsity via Alternating Direction Methods (Paper)</title>
<description>@article{13:48,author={Zhiwei Qin and Donald Goldfarb}, Title={Structured Sparsity via Alternating Direction Methods},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/qin12a/qin12a.pdf}}</description>
<link>https://academictorrents.com/download/076ca91cc7f0bd7bb802b6622a5d46ec49b280ea</link>
</item>
<item>
<title>An Introduction to Variable and Feature Selection (Kernel Machines Section) (Paper)</title>
<description>@article{3:45,author={Isabelle Guyon and Andr Elisseeff}, Title={An Introduction to Variable and Feature Selection
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/barnard03a/barnard03a.pdf}}</description>
<link>https://academictorrents.com/download/29d693fc13be423a38598a7d000c933185f29c90</link>
</item>
<item>
<title>Large-Sample Learning of Bayesian Networks is NP-Hard (Paper)</title>
<description>@article{5:47,author={David Maxwell Chickering and David Heckerman and Christopher Meek}, Title={Large-Sample Learning of Bayesian Networks is NP-Hard},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/huang04a/huang04a.pdf}}</description>
<link>https://academictorrents.com/download/9a4b9f335fc34e1926309b3c6d81900cefe850ce</link>
</item>
<item>
<title>Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2 (Paper)</title>
<description>@article{9:22,author={Onureena Banerjee and Laurent El Ghaoui and Alexandre d'Aspremont}, Title={Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/xie08a/xie08a.pdf}}</description>
<link>https://academictorrents.com/download/f62507cd6f2f5fd5a248b3f43706d8909be1b27d</link>
</item>
<item>
<title>Multi-Target Regression with Rule Ensembles (Paper)</title>
<description>@article{13:78,author={Timo Aho and Bernard enko and Sao Deroski and Tapio Elomaa}, Title={Multi-Target Regression with Rule Ensembles},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/aho12a/aho12a.pdf}}</description>
<link>https://academictorrents.com/download/e363d82858aa3aea8a32cad442a29ac8d243b333</link>
</item>
<item>
<title>Static Prediction Games for Adversarial Learning Problems (Paper)</title>
<description>@article{13:85,author={Michael Brckner and Christian Kanzow and Tobias Scheffer}, Title={Static Prediction Games for Adversarial Learning Problems},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/brueckner12a/brueckner12a.pdf}}</description>
<link>https://academictorrents.com/download/cdd39295089b63185ca2fb6009858297a3125d0b</link>
</item>
<item>
<title>Coupled Clustering: A Method for Detecting Structural Correspondence (Paper)</title>
<description>@article{3:30,author={Zvika Marx and Ido Dagan and Joachim M. Buhmann and Eli Shamir}, Title={Coupled Clustering: A Method for Detecting Structural Correspondence},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/ling02a/ling02a.pdf}}</description>
<link>https://academictorrents.com/download/31bb1d49aa29c536ff8f4edfbb1ae35fdf766135</link>
</item>
<item>
<title>The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces (Kernel Machines Section) (Paper)</title>
<description>@article{3:13,author={Masashi Sugiyama and Klaus-Robert Mller}, Title={The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/fung02a/fung02a.pdf}}</description>
<link>https://academictorrents.com/download/309dbeb9e5cd88d8bc7ad02d109dd5674549adea</link>
</item>
<item>
<title>High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion (Paper)</title>
<description>@article{13:76,author={Animashree Anandkumar and Vincent Y.F. Tan and Furong Huang and Alan S. Willsky}, Title={High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/anandkumar12a/anandkumar12a.pdf}}</description>
<link>https://academictorrents.com/download/85d39ec1436373da7c96212f48c59c7f50acc651</link>
</item>
<item>
<title>A New Algorithm for Estimating the Effective Dimension-Reduction Subspace (Paper)</title>
<description>@article{9:55,author={Ashwin Srinivasan and Ross D. King}, Title={A New Algorithm for Estimating the Effective Dimension-Reduction Subspace},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/zhang08a/zhang08a.pdf}}</description>
<link>https://academictorrents.com/download/b8da80a6506bb92ee0361848b4106e2de6aec52d</link>
</item>
<item>
<title>Multi-Instance Learning with Any Hypothesis Class (Paper)</title>
<description>@article{13:97,author={Sivan Sabato and Naftali Tishby}, Title={Multi-Instance Learning with Any Hypothesis Class},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/sabato12a/sabato12a.pdf}}</description>
<link>https://academictorrents.com/download/cd9c87f148a42e541d41ac5356c359309a8049f9</link>
</item>
<item>
<title>Randomized Variable Elimination (Paper)</title>
<description>@article{5:48,author={David J. Stracuzzi and Paul E. Utgoff}, Title={Randomized Variable Elimination},journal={Journal of Machine Learning Research},volume={5}, url={http://www.jmlr.org/papers/volume5/chickering04a/chickering04a.pdf}}</description>
<link>https://academictorrents.com/download/8177336f115e53e3de8a416b97194bfe26e84e6d</link>
</item>
<item>
<title>Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions (Paper)</title>
<description>@article{3:23,author={Alexander Strehl and Joydeep Ghosh}, Title={Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/chickering02b/erratum.pdf}}</description>
<link>https://academictorrents.com/download/f5c1c6514a5d6adb80fe84966ee28e5d8294f83d</link>
</item>
<item>
<title>Optimistic Bayesian Sampling in Contextual-Bandit Problems (Paper)</title>
<description>@article{13:67,author={Benedict C. May and Nathan Korda and Anthony Lee and David S. Leslie}, Title={Optimistic Bayesian Sampling in Contextual-Bandit Problems},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/may12a/may12a.pdf}}</description>
<link>https://academictorrents.com/download/0b5365e25cf26e1a7592b634ead614db82ed6031</link>
</item>
<item>
<title>NIMFA : A Python Library for Nonnegative Matrix Factorization (Paper)</title>
<description>@article{13:30,author={Marinka itnik and Bla Zupan}, Title={NIMFA : A Python Library for Nonnegative Matrix Factorization},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/zitnik12a/zitnik12a.pdf}}</description>
<link>https://academictorrents.com/download/636e004329b9da7452feab9e543c7bca2a3af65f</link>
</item>
<item>
<title>Finite-Sample Analysis of Least-Squares Policy Iteration (Paper)</title>
<description>@article{13:98,author={Alessandro Lazaric and Mohammad Ghavamzadeh and Rmi Munos}, Title={Finite-Sample Analysis of Least-Squares Policy Iteration},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/lazaric12a/lazaric12a.pdf}}</description>
<link>https://academictorrents.com/download/a0980d654eec0d65bfd5d0fb15c04141b46eb5c7</link>
</item>
<item>
<title>Ranking Individuals by Group Comparisons (Paper)</title>
<description>@article{9:74,author={Hannes Nickisch and Carl Edward Rasmussen}, Title={Ranking Individuals by Group Comparisons},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/biau08a/biau08a.pdf}}</description>
<link>https://academictorrents.com/download/3540028f12669876e8c36cc9cc7bbd38ed239e22</link>
</item>
<item>
<title>MedLDA: Maximum Margin Supervised Topic Models (Paper)</title>
<description>@article{13:74,author={Jun Zhu and Amr Ahmed and Eric P. Xing}, Title={MedLDA: Maximum Margin Supervised Topic Models},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/zhu12a/zhu12a.pdf}}</description>
<link>https://academictorrents.com/download/52bb0a07bf9bf6bda93e6178b42bcf3a4e9360a0</link>
</item>
<item>
<title>EP-GIG Priors and Applications in Bayesian Sparse Learning (Paper)</title>
<description>@article{13:65,author={Zhihua Zhang and Shusen Wang and Dehua Liu and Michael I. Jordan}, Title={EP-GIG Priors and Applications in Bayesian Sparse Learning},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/zhang12b/zhang12b.pdf}}</description>
<link>https://academictorrents.com/download/b590650675dc8b49b4562bd82144f5649c2f6b29</link>
</item>
<item>
<title>Learning Monotone DNF from a Teacher that Almost Does Not Answer Membership Queries (Paper)</title>
<description>@article{3:2,author={Nader H. Bshouty and Nadav Eiron}, Title={Learning Monotone DNF from a Teacher that Almost Does Not Answer Membership Queries},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/bshouty02a/bshouty02a.pdf}}</description>
<link>https://academictorrents.com/download/5a5ca85b282b3b1847523789aa7e164d9a7d83c6</link>
</item>
<item>
<title>Efficient Methods for Robust Classification Under Uncertainty in Kernel Matrices (Paper)</title>
<description>@article{13:94,author={Aharon Ben-Tal and Sahely Bhadra and Chiranjib Bhattacharyya and Arkadi Nemirovski}, Title={Efficient Methods for Robust Classification Under Uncertainty in Kernel Matrices},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/ben-tal12a/ben-tal12a.pdf}}</description>
<link>https://academictorrents.com/download/d7a243353eec534de466eeb3f2f40b51d9ae2895</link>
</item>
<item>
<title>A Case Study on Meta-Generalising: A Gaussian Processes Approach (Paper)</title>
<description>@article{13:24,author={Grigorios Skolidis and Guido Sanguinetti}, Title={A Case Study on Meta-Generalising: A Gaussian Processes Approach},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/skolidis12a/skolidis12a.pdf}}</description>
<link>https://academictorrents.com/download/98cf97e5ff54db7b6d12d0b4f98e7002feddad7e</link>
</item>
<item>
<title>Nonparametric Guidance of Autoencoder Representations using Label Information (Paper)</title>
<description>@article{13:83,author={Jasper Snoek and Ryan P. Adams and Hugo Larochelle}, Title={Nonparametric Guidance of Autoencoder Representations using Label Information},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/snoek12a/snoek12a.pdf}}</description>
<link>https://academictorrents.com/download/53924a521b9c9d67c89a2da3fa8e8f8f1f1c3937</link>
</item>
<item>
<title>Ranking a Random Feature for Variable and Feature Selection (Paper)</title>
<description>@article{3:57,author={Herv Stoppiglia and Grard Dreyfus and Rmi Dubois and Yacine Oussar}, Title={Ranking a Random Feature for Variable and Feature Selection},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/rakotomamonjy03a/rakotomamonjy03a.pdf}}</description>
<link>https://academictorrents.com/download/c65d372e2a2e00f1ff3608764a18a66708ed5cab</link>
</item>
<item>
<title>Optimization Techniques for Semi-Supervised Support Vector Machines (Paper)</title>
<description>@article{9:9,author={Yoav Freund and Robert E. Schapire}, Title={Optimization Techniques for Semi-Supervised Support Vector Machines},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/buja08a/buja08a.pdf}}</description>
<link>https://academictorrents.com/download/239d78675e2c2f64f5d6544ff85b5d206f451a26</link>
</item>
<item>
<title>On Boosting with Polynomially Bounded Distributions (Paper)</title>
<description>@article{3:20,author={Nader H. Bshouty and Dmitry Gavinsky}, Title={On Boosting with Polynomially Bounded Distributions},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/bartlett02a/bartlett02a.pdf}}</description>
<link>https://academictorrents.com/download/329dd577181e340c38c987edcf47906ce5700777</link>
</item>
<item>
<title>JNCC2: The Java Implementation Of Naive Credal Classifier 2(Machine Learning Open Source Software Paper) (Paper)</title>
<description>@article{9:92,author={Franois Fleuret and Donald Geman}, Title={JNCC2: The Java Implementation Of Naive Credal Classifier 2(Machine Learning Open Source Software Paper)},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/he08a/he08a.pdf}}</description>
<link>https://academictorrents.com/download/abe316b76c07662377614dd1d3deeaf7bbd4abfb</link>
</item>
<item>
<title>Efficient Algorithms for Decision Tree Cross-validation (Paper)</title>
<description>@article{3:25,author={Hendrik Blockeel and Jan Struyf}, Title={Efficient Algorithms for Decision Tree Cross-validation},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/strehl02a/strehl02a.pdf}}</description>
<link>https://academictorrents.com/download/f6084af01c3b5f141357da7650a46652b7819e22</link>
</item>
<item>
<title>Learning Symbolic Representations of Hybrid Dynamical Systems (Paper)</title>
<description>@article{13:115,author={Daniel L. Ly and Hod Lipson}, Title={Learning Symbolic Representations of Hybrid Dynamical Systems},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/ly12a/ly12a.pdf}}</description>
<link>https://academictorrents.com/download/f41f4f536d2b60782911e1bfa257078d23465384</link>
</item>
<item>
<title>An Introduction to Artificial Prediction Markets for Classification (Paper)</title>
<description>@article{13:71,author={Adrian Barbu and Nathan Lay}, Title={An Introduction to Artificial Prediction Markets for Classification},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/barbu12a/barbu12a.pdf}}</description>
<link>https://academictorrents.com/download/d410f824cb7401a3b0f341279e0e4947cc5f6105</link>
</item>
<item>
<title>Rejoinder to Reponses to Evidence Contrary to the Statistical View of Boosting (Paper)</title>
<description>@article{9:8,author={Andreas Buja and Werner Stuetzle}, Title={Rejoinder to Reponses to Evidence Contrary to the Statistical View of Boosting},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/bennett08a/bennett08a.pdf}}</description>
<link>https://academictorrents.com/download/afff4d2d8d6638904760b72dfd7025a031e7562d</link>
</item>
<item>
<title>An Improved GLMNET for L1-regularized Logistic Regression (Paper)</title>
<description>@article{13:64,author={Guo-Xun Yuan and Chia-Hua Ho and Chih-Jen Lin}, Title={An Improved GLMNET for L1-regularized Logistic Regression},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/yuan12a/yuan12a.pdf}}</description>
<link>https://academictorrents.com/download/7e2714ef3c9935f7b69f3d340e2c87de325b2cd2</link>
</item>
<item>
<title>Positive Semidefinite Metric Learning Using Boosting-like Algorithms (Paper)</title>
<description>@article{13:35,author={Chunhua Shen and Junae Kim and Lei Wang and Anton van den Hengel}, Title={Positive Semidefinite Metric Learning Using Boosting-like Algorithms},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/shen12a/shen12a.pdf}}</description>
<link>https://academictorrents.com/download/4b9d141736b5842aa7e384fc1e83e7acd5f815b1</link>
</item>
<item>
<title>Kernel Methods for Relation Extraction (Paper)</title>
<description>@article{3:42,author={Dmitry Zelenko and Chinatsu Aone and Anthony Richardella}, Title={Kernel Methods for Relation Extraction},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/crammer03b/crammer03b.pdf}}</description>
<link>https://academictorrents.com/download/7856d9f782cdb73ed80434bc67777063090a0032</link>
</item>
<item>
<title>Structured Sparsity and Generalization (Paper)</title>
<description>@article{13:23,author={Andreas Maurer and Massimiliano Pontil}, Title={Structured Sparsity and Generalization},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/maurer12a/maurer12a.pdf}}</description>
<link>https://academictorrents.com/download/ceebd84f2e899b59b7210e16cca72f598dffd293</link>
</item>
<item>
<title>Learning to Construct Fast Signal Processing Implementations (Paper)</title>
<description>@article{3:35,author={Bryan Singer and Manuela Veloso}, Title={Learning to Construct Fast Signal Processing Implementations},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/scheffer02a/scheffer02a.pdf}}</description>
<link>https://academictorrents.com/download/f7ae3b62362210a54498015cbc5f94db1ea2ee4d</link>
</item>
<item>
<title>Use of the Zero-Norm with Linear Models and Kernel Methods (Kernel Machines Section) (Paper)</title>
<description>@article{3:59,author={Jason Weston and Andr Elisseeff and Bernhard Schlkopf and Mike Tipping}, Title={Use of the Zero-Norm with Linear Models and Kernel Methods
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/rivals03a/rivals03a.pdf}}</description>
<link>https://academictorrents.com/download/8a36766d87a75f389fa1aa990fa71f0731363b9a</link>
</item>
<item>
<title>Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning (Paper)</title>
<description>@article{13:55,author={Sangkyun Lee and Stephen J. Wright}, Title={Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/lee12a/lee12a.pdf}}</description>
<link>https://academictorrents.com/download/757245e31fd10a11c8794d3384dbffbdf97fd1e1</link>
</item>
<item>
<title>Distributional Word Clusters vs. Words for Text Categorization (Kernel Machines Section) (Paper)</title>
<description>@article{3:46,author={Ron Bekkerman and Ran El-Yaniv and Naftali Tishby and Yoad Winter}, Title={Distributional Word Clusters vs. Words for Text Categorization
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf}}</description>
<link>https://academictorrents.com/download/658627300dd23648f58177f238327eb0832871c1</link>
</item>
<item>
<title>Online Submodular Minimization (Paper)</title>
<description>@article{13:93,author={Elad Hazan and Satyen Kale}, Title={Online Submodular Minimization},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/hazan12a/hazan12a.pdf}}</description>
<link>https://academictorrents.com/download/5533d2cdec46d72ab67cc9126cf262b8ac6be883</link>
</item>
<item>
<title>Structural Learning of Chain Graphs via Decomposition (Paper)</title>
<description>@article{9:97,author={Giorgio Corani and Marco Zaffalon}, Title={Structural Learning of Chain Graphs via Decomposition},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/bab08a/bab08a.pdf}}</description>
<link>https://academictorrents.com/download/677280641c8076dda3e04887360a272b2405ab0a</link>
</item>
<item>
<title>A Tutorial on Conformal Prediction (Paper)</title>
<description>@article{9:14,author={Olivier Chapelle and Vikas Sindhwani and Sathiya S. Keerthi}, Title={A Tutorial on Conformal Prediction},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/mease08b/mease08b.pdf}}</description>
<link>https://academictorrents.com/download/350b9cfb3613f55812577cf980465694de9cdc74</link>
</item>
<item>
<title>Stationary Features and Cat Detection (Paper)</title>
<description>@article{9:87,author={Xing Sun and Andrew B. Nobel}, Title={Stationary Features and Cat Detection},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/lebanon08a/lebanon08a.pdf}}</description>
<link>https://academictorrents.com/download/5b025381a593836a068f13e3b07f039dd4e5222f</link>
</item>
<item>
<title>-MDPs: Learning in Varying Environments (Paper)</title>
<description>@article{3:7,author={Istvn Szita and Blint Takcs and Andrs Lrincz}, Title={-MDPs: Learning in Varying Environments},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/szita02a/szita02a.pdf}}</description>
<link>https://academictorrents.com/download/3634303099110d62a04931a61d765db4eafe994d</link>
</item>
<item>
<title>Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Subcellular Location Patterns (Paper)</title>
<description>@article{9:25,author={Gerda Claeskens and Christophe Croux and Johan Van Kerckhoven}, Title={Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Subcellular Location Patterns},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/jiang08a/jiang08a.pdf}}</description>
<link>https://academictorrents.com/download/a84843e9bb71109eff0386b39acb4f70a58b4d04</link>
</item>
<item>
<title>Rademacher and Gaussian Complexities: Risk Bounds and Structural Results (Paper)</title>
<description>@article{3:19,author={Peter L. Bartlett and Shahar Mendelson}, Title={Rademacher and Gaussian Complexities: Risk Bounds and Structural Results},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/bendavid02a/bendavid02a.pdf}}</description>
<link>https://academictorrents.com/download/e2c999aee9996bdd2ec49ee83c38cf51680c4e45</link>
</item>
<item>
<title>Learning Probabilistic Models of Link Structure (Paper)</title>
<description>@article{3:27,author={Lisa Getoor and Nir Friedman and Daphne Koller and Benjamin Taskar}, Title={Learning Probabilistic Models of Link Structure},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/blockeel02a/blockeel02a.pdf}}</description>
<link>https://academictorrents.com/download/05efe66eb343ac4873beadfff6fc89df0ab83eb4</link>
</item>
<item>
<title>DEAP: Evolutionary Algorithms Made Easy (Paper)</title>
<description>@article{13:70,author={Flix-Antoine Fortin and Franois-Michel De Rainville and Marc-Andr Gardner and Marc Parizeau and Christian Gagn}, Title={DEAP: Evolutionary Algorithms Made Easy},journal={Journal of Machine Learning Research},volume={13}, url={http://www.jmlr.org/papers/volume13/fortin12a/fortin12a.pdf}}</description>
<link>https://academictorrents.com/download/ac9a858e4781b9b8a0acb2d12a4ef291e6509fa9</link>
</item>
<item>
<title>Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem (Paper)</title>
<description>@article{3:34,author={Marc Sebban and Richard Nock and Stphane Lallich}, Title={Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/perkins02a/perkins02a.pdf}}</description>
<link>https://academictorrents.com/download/0de748dc086791b5cde497b0dca4e896614a7d69</link>
</item>
<item>
<title>The Set Covering Machine (Paper)</title>
<description>@article{3:29,author={Mario Marchand and John Shawe-Taylor}, Title={The Set Covering Machine},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/getoor02a/getoor02a.pdf}}</description>
<link>https://academictorrents.com/download/d7e14370bebe943dc6a4cef89a742de52a6a28c4</link>
</item>
<item>
<title>Learning Precise Timing with LSTM Recurrent Networks (Paper)</title>
<description>@article{3:6,author={Felix A. Gers and Nicol N. Schraudolph and Jrgen Schmidhuber}, Title={Learning Precise Timing with LSTM Recurrent Networks},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/gers02a/gers02a.pdf}}</description>
<link>https://academictorrents.com/download/f018ed13be7e4ca2f00b740524c3b36d5196cf55</link>
</item>
<item>
<title>Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space (Kernel Machines Section) (Paper)</title>
<description>@article{3:53,author={Simon Perkins and Kevin Lacker and James Theiler}, Title={Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/forman03a/forman03a.pdf}}</description>
<link>https://academictorrents.com/download/f31b0ef2048424d3b7b66538d6c4da6f7a3c5db5</link>
</item>
<item>
<title>A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification (Kernel Machines Section) (Paper)</title>
<description>@article{3:50,author={Inderjit S. Dhillon and Subramanyam Mallela and Rahul Kumar}, Title={A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/bi03a/bi03a.pdf}}</description>
<link>https://academictorrents.com/download/244c23e3a7062823ef49de70eab0eb51f2c3468c</link>
</item>
<item>
<title>Support Vector Machinery for Infinite Ensemble Learning (Paper)</title>
<description>@article{9:11,author={Peter J. Bickel and Ya'acov Ritov}, Title={Support Vector Machinery for Infinite Ensemble Learning},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/friedman08a/friedman08a.pdf}}</description>
<link>https://academictorrents.com/download/13837aac7d63d8f20681f7e6d6af6d0495b06cc7</link>
</item>
<item>
<title>Online Learning of Complex Prediction Problems Using Simultaneous Projections (Paper)</title>
<description>@article{9:48,author={Jrg Lcke and  Maneesh Sahani}, Title={Online Learning of Complex Prediction Problems Using Simultaneous Projections},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/bach08b/bach08b.pdf}}</description>
<link>https://academictorrents.com/download/f784e9f849bc0f4142145161b23ec7cf181ef026</link>
</item>
<item>
<title>Closed Sets for Labeled Data (Paper)</title>
<description>@article{9:21,author={Xianchao Xie and Zhi Geng}, Title={Closed Sets for Labeled Data},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/panait08a/panait08a.pdf}}</description>
<link>https://academictorrents.com/download/bc40153090478105e03845c9168eef0fe41de929</link>
</item>
<item>
<title>Value Function Approximation using Multiple Aggregation for Multiattribute Resource Management (Paper)</title>
<description>@article{9:70,author={Yair Goldberg and Alon Zakai and Dan Kushnir and Ya'acov Ritov}, Title={Value Function Approximation using Multiple Aggregation for Multiattribute Resource Management},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/braun08a/braun08a.pdf}}</description>
<link>https://academictorrents.com/download/5950e2f9531fcafd052a29e6253fced3d0e29fe6</link>
</item>
<item>
<title>Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective (Paper)</title>
<description>@article{9:15,author={Andreas Krause and Ajit Singh and Carlos Guestrin}, Title={Theoretical Advantages of Lenient Learners:  An Evolutionary Game Theoretic Perspective},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/chapelle08a/chapelle08a.pdf}}</description>
<link>https://academictorrents.com/download/378f768db789e965e18e82e6537655a4bd37fe12</link>
</item>
<item>
<title>Multiple-Instance Learning of Real-Valued Data (Paper)</title>
<description>@article{3:26,author={Daniel R. Dooly and Qi Zhang and Sally A. Goldman and Robert A. Amar}, Title={Multiple-Instance Learning of Real-Valued Data},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/brodley02a/brodley02a.pdf}}</description>
<link>https://academictorrents.com/download/936a92932c01c3f5e9994ae8bd2115f4ccb4adc9</link>
</item>
<item>
<title>Multi-Agent Reinforcement Learning in Common Interest and Fixed Sum Stochastic Games: An Experimental Study (Paper)</title>
<description>@article{9:90,author={Alain Rakotomamonjy and Francis R. Bach and Stphane Canu and Yves Grandvalet}, Title={Multi-Agent Reinforcement Learning in Common Interest and Fixed Sum Stochastic Games: An Experimental Study},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/loog08a/loog08a.pdf}}</description>
<link>https://academictorrents.com/download/905a1856ae59c83e46961c39774a98d5e93b6f5d</link>
</item>
<item>
<title>Aggregation of SVM Classifiers Using Sobolev Spaces (Paper)</title>
<description>@article{9:52,author={Kai-Wei Chang and Cho-Jui Hsieh and Chih-Jen Lin}, Title={Aggregation of SVM Classifiers Using Sobolev Spaces},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/koo08a/koo08a.pdf}}</description>
<link>https://academictorrents.com/download/a717b062ca3e26c659a513eb1cf48a2d7e760697</link>
</item>
<item>
<title>Minimal Kernel Classifiers (Kernel Machines Section) (Paper)</title>
<description>@article{3:12,author={Glenn M. Fung and Olvi L. Mangasarian and Alexander J. Smola}, Title={Minimal Kernel Classifiers
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/nevo02a/nevo02a.pdf}}</description>
<link>https://academictorrents.com/download/6c07eeed3d15b409e1e61afb2f35998f787f678e</link>
</item>
<item>
<title>Mixed Membership Stochastic Blockmodels (Paper)</title>
<description>@article{9:67,author={Leonor Becerra-Bonache and Colin de la Higuera and Jean-Christophe Janodet and Frdric Tantini}, Title={Mixed Membership Stochastic Blockmodels},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/bartlett08a/bartlett08a.pdf}}</description>
<link>https://academictorrents.com/download/2d9fa4c14ce0ea510fcbb35cdf2c1c026596dcaf</link>
</item>
<item>
<title>Data-dependent margin-based generalization bounds for classification (Paper)</title>
<description>@article{3:4,author={Andrs Antos and Balzs Kgl and Tams Linder and Gbor Lugosi}, Title={Data-dependent margin-based generalization bounds for classification},journal={Journal of Machine Learning Research},volume={3}, url={http://www.jmlr.org/papers/volume3/antos02a/antos02a.pdf}}</description>
<link>https://academictorrents.com/download/28a4a9d084522d1bf2ffeb3163e2e771a8f8f34c</link>
</item>
<item>
<title>Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming (Paper)</title>
<description>@article{9:50,author={Jean-Philippe Pellet and Andr Elisseeff}, Title={Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/aspremont08a/aspremont08a.pdf}}</description>
<link>https://academictorrents.com/download/28acd76f57bb2194087ef9a57b13d6a4adb2edb5</link>
</item>
<item>
<title>Causal Reasoning with Ancestral Graphs(Special Topic on Causality) (Paper)</title>
<description>@article{9:49,author={Alexandre d'Aspremont and Francis Bach and Laurent El Ghaoui}, Title={Causal Reasoning with Ancestral Graphs(Special Topic on Causality)},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/luecke08a/luecke08a.pdf}}</description>
<link>https://academictorrents.com/download/b19ab3f5b9d697fabd43259db5dc42948356adc3</link>
</item>
<item>
<title>Robust Submodular Observation Selection (Paper)</title>
<description>@article{9:95,author={Avraham Bab and Ronen I. Brafman}, Title={Robust Submodular Observation Selection},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a-supplement.pdf}}</description>
<link>https://academictorrents.com/download/eaf6521c44b5ca8db8d93c57b324ff4c35b34f32</link>
</item>
<item>
<title>Magic Moments for Structured Output Prediction (Paper)</title>
<description>@article{9:96,author={Salvador Garca and Francisco Herrera}, Title={Magic Moments for Structured Output Prediction},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/koo08b/koo08b.pdf}}</description>
<link>https://academictorrents.com/download/1c4fbdb2caa3b21d17d20cb8c001e434ceaec178</link>
</item>
<item>
<title>Search for Additive Nonlinear Time Series Causal Models (Paper)</title>
<description>@article{9:34,author={Rmi Munos and Csaba Szepesvri}, Title={Search for Additive Nonlinear Time Series Causal Models},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/seeger08a/seeger08a.pdf}}</description>
<link>https://academictorrents.com/download/bc6a95f3a1ec7d89e52d1e6a4ae661dbaa769985</link>
</item>
<item>
<title>A Moment Bound for Multi-hinge Classifiers (Paper)</title>
<description>@article{9:73,author={Grard Biau and Luc Devroye and Gbor Lugosi}, Title={A Moment Bound for Multi-hinge Classifiers},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/airoldi08a/airoldi08a.pdf}}</description>
<link>https://academictorrents.com/download/ebe3027120419d2ae25dee559946f954b0271be0</link>
</item>
<item>
<title>Learning Balls of Strings from Edit Corrections (Paper)</title>
<description>@article{9:62,author={Arthur D. Szlam and Mauro Maggioni and Ronald R. Coifman}, Title={Learning Balls of Strings from Edit Corrections},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/csaji08a/csaji08a.pdf}}</description>
<link>https://academictorrents.com/download/6e8a90310cafa2984f6d6754ad83a60d5b35e5f2</link>
</item>
<item>
<title>Value Function Based Reinforcement Learning in Changing Markovian Environments (Paper)</title>
<description>@article{9:56,author={Manu Chhabra and Robert A. Jacobs}, Title={Value Function Based Reinforcement Learning in Changing Markovian Environments},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/srinivasan08a/srinivasan08a.pdf}}</description>
<link>https://academictorrents.com/download/26d1ed2e2fae81bb304ee38baeb6ac7da155d7ed</link>
</item>
<item>
<title>An Error Bound Based on a Worst Likely Assignment (Paper)</title>
<description>@article{9:30,author={Shann-Ching Chen and Geoffrey J. Gordon and Robert F. Murphy}, Title={An Error Bound Based on a Worst Likely Assignment},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/lin08b/lin08b.pdf}}</description>
<link>https://academictorrents.com/download/b731c15f0c42e454ec96775d8f3a6ddcd6409ff9</link>
</item>
<item>
<title>Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data (Paper)</title>
<description>@article{9:17,author={Suhrid Balakrishnan and David Madigan}, Title={Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/lin08a/lin08a.pdf}}</description>
<link>https://academictorrents.com/download/76ea1046a70ebe6bfacd53fee64ed6e22835391d</link>
</item>
<item>
<title>Probabilistic Characterization of Random Decision Trees (Paper)</title>
<description>@article{9:78,author={Bernadetta Tarigan and Sara A. van de Geer}, Title={Probabilistic Characterization of Random Decision Trees},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/jambeiro08a/jambeiro08a.pdf}}</description>
<link>https://academictorrents.com/download/cf742ce887877ed5fd5e5f52526bcbe30c222b00</link>
</item>
<item>
<title>Learning to Combine Motor Primitives Via Greedy Additive Regression (Paper)</title>
<description>@article{9:51,author={Ja-Yong Koo and Yoonkyung Lee and Yuwon Kim and Changyi Park}, Title={Learning to Combine Motor Primitives Via Greedy Additive Regression},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/pellet08a/pellet08a.pdf}}</description>
<link>https://academictorrents.com/download/66a93322d0218625ab3e86c285af5e15bf051c09</link>
</item>
<item>
<title>An Information Criterion for Variable Selection in Support Vector Machines(Special Topic on Model Selection) (Paper)</title>
<description>@article{9:20,author={Liviu Panait and Karl Tuyls and Sean Luke}, Title={An Information Criterion for Variable Selection in Support Vector Machines(Special Topic on Model Selection)},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/shafer08a/shafer08a.pdf}}</description>
<link>https://academictorrents.com/download/ce3807fa7cfe63dc402cea83a8f5d900fd7cd458</link>
</item>
<item>
<title>SimpleMKL (Paper)</title>
<description>@article{9:85,author={Michiel Debruyne and Mia Hubert and Johan A.K. Suykens}, Title={SimpleMKL},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/krupka08b/krupka08b.pdf}}</description>
<link>https://academictorrents.com/download/9a7b033a7a12876bb4b7b2376d9b28e1518cbe99</link>
</item>
<item>
<title>Finite-Time Bounds for Fitted Value Iteration (Paper)</title>
<description>@article{9:29,author={Chih-Jen Lin and Ruby C. Weng and S. Sathiya Keerthi}, Title={Finite-Time Bounds for Fitted Value Iteration},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/klanke08a/klanke08a.pdf}}</description>
<link>https://academictorrents.com/download/5e3d93e553bb1471433e36e94ad686d07f2b3eca</link>
</item>
<item>
<title>Learning from Multiple Sources (Paper)</title>
<description>@article{9:59,author={Andrea Caponnetto and Charles A. Micchelli and Massimiliano Pontil and Yiming Ying}, Title={Learning from Multiple Sources},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/zhu08a/zhu08a.pdf}}</description>
<link>https://academictorrents.com/download/a91d148d082332f0c0b2231c6a08cc7c2869bf69</link>
</item>
<item>
<title>Randomized Online PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension (Paper)</title>
<description>@article{9:77,author={Jorge Jambeiro Filho and Jacques Wainer}, Title={Randomized Online PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/dietterich08a/dietterich08a.pdf}}</description>
<link>https://academictorrents.com/download/1b397cb54c2bb0378b38b64935ea868a8a109183</link>
</item>
<item>
<title>Bayesian Inference and Optimal Design for the Sparse Linear Model (Paper)</title>
<description>@article{9:28,author={Stefan Klanke and Sethu Vijayakumar and Stefan Schaal}, Title={Bayesian Inference and Optimal Design for the Sparse Linear Model},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/corani08a/corani08a.pdf}}</description>
<link>https://academictorrents.com/download/cd9896d409b4ff16a1c2bcb1d3c51ec2147787ae</link>
</item>
<item>
<title>Finding Optimal Bayesian Network Given a Super-Structure (Paper)</title>
<description>@article{9:76,author={Thomas G. Dietterich and Guohua Hao and Adam Ashenfelter}, Title={Finding Optimal Bayesian Network Given a Super-Structure},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/george08a/george08a.pdf}}</description>
<link>https://academictorrents.com/download/59975e2fe7358266a56dcf58b568c8792749dfaa</link>
</item>
<item>
<title>Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers (Paper)</title>
<description>@article{9:19,author={Glenn Shafer and Vladimir Vovk}, Title={Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/krupka08a/krupka08a.pdf}}</description>
<link>https://academictorrents.com/download/903a5ecb011e8d9af24933ebe07cbe50e1f07be2</link>
</item>
<item>
<title>Gradient Tree Boosting for Training Conditional Random Fields (Paper)</title>
<description>@article{9:71,author={Ilya Shpitser and Judea Pearl}, Title={Gradient Tree Boosting for Training Conditional Random Fields},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/goldberg08a/goldberg08a.pdf}}</description>
<link>https://academictorrents.com/download/7199cf9eaca3c451860febeafe48565ea1b35a54</link>
</item>
<item>
<title>Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks (Paper)</title>
<description>@article{9:60,author={Arnak S. Dalalyan and Anatoly Juditsky and Vladimir Spokoiny}, Title={Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/caponnetto08a/caponnetto08a.pdf}}</description>
<link>https://academictorrents.com/download/07778ccabf041692ac10728350a99310ad4bcce0</link>
</item>
<item>
<title>Active Learning by Spherical Subdivision (Paper)</title>
<description>@article{9:5,author={Falk-Florian Henrich and Klaus Obermayer}, Title={Active Learning by Spherical Subdivision},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/henrich08a/henrich08a.pdf}}</description>
<link>https://academictorrents.com/download/9e38b71ba22a194b1ed0008950d2fef430f7b8e8</link>
</item>
<item>
<title>Nearly Uniform Validation Improves Compression-Based Error Bounds (Paper)</title>
<description>@article{9:58,author={Jun Zhu and Zaiqing Nie and Bo Zhang and Ji-Rong Wen}, Title={Nearly Uniform Validation Improves Compression-Based Error Bounds},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/loustau08a/loustau08a.pdf}}</description>
<link>https://academictorrents.com/download/6dee60ca650e69104ca00cddcfa37d1bf4f24662</link>
</item>
<item>
<title>Discriminative Learning of Max-Sum Classifiers (Paper)</title>
<description>@article{9:4,author={Vojtch Franc and Bogdan Savchynskyy}, Title={Discriminative Learning of Max-Sum Classifiers},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/franc08a/franc08a.pdf}}</description>
<link>https://academictorrents.com/download/375896cb5a477b789da29b77101d39cbda5ac916</link>
</item>
<item>
<title>Multi-class Discriminant Kernel Learning via Convex Programming(Special Topic on Model Selection) (Paper)</title>
<description>@article{9:27,author={Giorgio Corani and Marco Zaffalon}, Title={Multi-class Discriminant Kernel Learning via Convex Programming(Special Topic on Model Selection)},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/garriga08a/garriga08a.pdf}}</description>
<link>https://academictorrents.com/download/2564fc98f00cee827e978026078e996d71540995</link>
</item>
<item>
<title>Automatic PCA Dimension Selection for High Dimensional Data and Small Sample Sizes (Paper)</title>
<description>@article{9:94,author={Imhoi Koo and Rhee Man Kil}, Title={Automatic PCA Dimension Selection for High Dimensional Data and Small Sample Sizes},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf}}</description>
<link>https://academictorrents.com/download/d5c9aa310bbaa11f7ce67c5293e760e558207019</link>
</item>
<item>
<title>Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction (Paper)</title>
<description>@article{9:53,author={Yonatan Amit and Shai Shalev-Shwartz and Yoram Singer}, Title={Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/chang08a/chang08a.pdf}}</description>
<link>https://academictorrents.com/download/64a22f796592c79060c833e8f4f43b57a58e22df</link>
</item>
<item>
<title>Ranking Categorical Features Using Generalization Properties (Paper)</title>
<description>@article{9:39,author={Tianjiao Chu and Clark Glymour}, Title={Ranking Categorical Features Using Generalization Properties},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/gomez08a/gomez08a.pdf}}</description>
<link>https://academictorrents.com/download/944422ba09b0a06697855b56bdb2cbb8d7d8c399</link>
</item>
<item>
<title>Visualizing Data using t-SNE (Paper)</title>
<description>@article{9:88,author={Kun Zhang and Laiwan Chan}, Title={Visualizing Data using t-SNE},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/sun08a/sun08a.pdf}}</description>
<link>https://academictorrents.com/download/9637de2f50952d9d8f52ac301ef04adef7fc7e4e</link>
</item>
<item>
<title>Non-Parametric Modeling of Partially Ranked Data (Paper)</title>
<description>@article{9:81,author={Eric Perrier and Seiya Imoto and Satoru Miyano}, Title={Non-Parametric Modeling of Partially Ranked Data},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/li08a/li08a.pdf}}</description>
<link>https://academictorrents.com/download/530abae83beab7b0c28ada2b2e0c05c501368e39</link>
</item>
<item>
<title>Learning Similarity with Operator-valued Large-margin Classifiers (Paper)</title>
<description>@article{9:38,author={Faustino Gomez and Jrgen Schmidhuber and Risto Miikkulainen}, Title={Learning Similarity with Operator-valued Large-margin Classifiers},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/christmann08a/christmann08a.pdf}}</description>
<link>https://academictorrents.com/download/fe7472920bcae4ff936715f160f4d19a91500a45</link>
</item>
<item>
<title>Trust Region Newton Method for Logistic Regression (Paper)</title>
<description>@article{9:24,author={Bo Jiang and Xuegong Zhang and Tianxi Cai}, Title={Trust Region Newton Method for Logistic Regression},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/ye08a/ye08a.pdf}}</description>
<link>https://academictorrents.com/download/6c04924c144aa652a22729992fc90c87029fbd5e</link>
</item>
<item>
<title>Linear-Time Computation of Similarity Measures for Sequential Data (Paper)</title>
<description>@article{9:2,author={Konrad Rieck and Pavel Laskov}, Title={Linear-Time Computation of Similarity Measures for Sequential Data},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/rieck08a/rieck08a.pdf}}</description>
<link>https://academictorrents.com/download/0b94baebf5c94c37b3aac5ae0b1ea64e45712537</link>
</item>
<item>
<title>On Relevant Dimensions in Kernel Feature Spaces (Paper)</title>
<description>@article{9:64,author={Koby Crammer and Michael Kearns and Jennifer Wortman}, Title={On Relevant Dimensions in Kernel Feature Spaces},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/bax08b/bax08b.pdf}}</description>
<link>https://academictorrents.com/download/774436c940f047c67a4c7266b56706f0a0725f76</link>
</item>
<item>
<title>Learning Control Knowledge for Forward Search Planning (Paper)</title>
<description>@article{9:26,author={Gemma C. Garriga and Petra Kralj and Nada Lavra}, Title={Learning Control Knowledge for Forward Search Planning},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/claeskens08a/claeskens08a.pdf}}</description>
<link>https://academictorrents.com/download/ca5b3f4c71025c801cf3383bf4ade2e3a09f2f54</link>
</item>
<item>
<title>On the Size and Recovery of Submatrices of Ones in a Random Binary Matrix (Paper)</title>
<description>@article{9:82,author={Manfred K. Warmuth and Dima Kuzmin}, Title={On the Size and Recovery of Submatrices of Ones in a Random Binary Matrix},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/perrier08a/perrier08a.pdf}}</description>
<link>https://academictorrents.com/download/8395af658162b29ab6723ab13822a7ca17d6341b</link>
</item>
<item>
<title>On the Suitable Domain for SVM Training in Image Coding (Paper)</title>
<description>@article{9:3,author={Gustavo Camps-Valls and Juan Gutirrez and Gabriel Gmez-Prez and Jess Malo}, Title={On the Suitable Domain for SVM Training in Image Coding},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/camps-valls08a/camps-valls08a.pdf}}</description>
<link>https://academictorrents.com/download/81a47ae86153d940580a01017b040da445bc28da</link>
</item>
<item>
<title>Learning to Select Features using their Properties (Paper)</title>
<description>@article{9:79,author={Tzu-Kuo Huang and Chih-Jen Lin and Ruby C. Weng}, Title={Learning to Select Features using their Properties},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/tarigan08a/tarigan08a.pdf}}</description>
<link>https://academictorrents.com/download/0baade895c487724dc4e1bb3c00ac01436c9f767</link>
</item>
<item>
<title>A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters (Paper)</title>
<description>@article{9:40,author={Christian Igel and Verena Heidrich-Meisner and Tobias Glasmachers}, Title={A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/chu08a/chu08a.pdf}}</description>
<link>https://academictorrents.com/download/72be7eb406e778d33a811ee00b4c2f16bb298670</link>
</item>
<item>
<title>HPB: A Model for Handling BN Nodes with High Cardinality Parents (Paper)</title>
<description>@article{9:72,author={Edoardo M. Airoldi and David M. Blei and Stephen E. Fienberg and Eric P. Xing}, Title={HPB: A Model for Handling BN Nodes with High Cardinality Parents},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/shpitser08a/shpitser08a.pdf}}</description>
<link>https://academictorrents.com/download/dd8607dea92a1f40c2fbf0ced40717a4101d15ef</link>
</item>
<item>
<title>Model Selection in Kernel Based Regression using the Influence Function(Special Topic on Model Selection) (Paper)</title>
<description>@article{9:80,author={Jia Li and Andrew W. Moore}, Title={Model Selection in Kernel Based Regression using the Influence Function(Special Topic on Model Selection)},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/huang08a/huang08a.pdf}}</description>
<link>https://academictorrents.com/download/0db504995fe64f0dca400a11f146ba007da5bcce</link>
</item>
<item>
<title>Minimal Nonlinear Distortion Principle for Nonlinear Independent Component Analysis (Paper)</title>
<description>@article{9:83,author={Amit Dhurandhar and Alin Dobra}, Title={Minimal Nonlinear Distortion Principle for Nonlinear Independent Component Analysis},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/warmuth08a/warmuth08a.pdf}}</description>
<link>https://academictorrents.com/download/04b87a940d20054d7b8e8517b07594be39a22451</link>
</item>
<item>
<title>Complete Identification Methods for the Causal Hierarchy(Special Topic on Causality) (Paper)</title>
<description>@article{9:66,author={Peter L. Bartlett and Marten H. Wegkamp}, Title={Complete Identification Methods for the Causal Hierarchy(Special Topic on Causality)},journal={Journal of Machine Learning Research},volume={9}, url={http://www.jmlr.org/papers/volume9/collins08a/collins08a.pdf}}</description>
<link>https://academictorrents.com/download/494f67d57abb4a2b2a1569d0957c8465737e35a2</link>
</item>
<item>
<title>On the Consistency of Multiclass Classification Methods (Special Topic on the Conference on Learning Theory 2005) (Paper)</title>
<description>@article{8:36,author={Ambuj Tewari and Peter L. Bartlett}, Title={On the Consistency of Multiclass Classification Methods (Special Topic on the Conference on Learning Theory 2005)},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/tewari07a/tewari07a.pdf}}</description>
<link>https://academictorrents.com/download/7858fdf307d9fe94aeaaeaeadfc554988b80a3ce</link>
</item>
<item>
<title>A Stochastic Algorithm for Feature Selection in Pattern Recognition (Paper)</title>
<description>@article{8:19,author={Sbastien Gadat and Laurent Younes}, Title={A Stochastic Algorithm for Feature Selection in Pattern Recognition},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/gadat07a/gadat07a.pdf}}</description>
<link>https://academictorrents.com/download/cdb0f212f1ab2bfc77c8280174c2a32bfc640cf0</link>
</item>
<item>
<title>Efficient Margin Maximizing with Boosting (Paper)</title>
<description>@article{6:71,author={Gunnar Rtsch and Manfred K. Warmuth}, Title={Efficient Margin Maximizing with Boosting},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/ratsch05a/ratsch05a.pdf}}</description>
<link>https://academictorrents.com/download/0a1853a62449b6b58acdcf59914841735ffbb8e6</link>
</item>
<item>
<title>Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition (Paper)</title>
<description>@article{7:13,author={Ron Begleiter and Ran El-Yaniv}, Title={Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/begleiter06a/begleiter06a.pdf}}</description>
<link>https://academictorrents.com/download/42c544de8a7f3e8a6819431fec63283f1add5fe0</link>
</item>
<item>
<title>Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks (Paper)</title>
<description>@article{10:50,author={Jean Hausser and Korbinian Strimmer}, Title={Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/hausser09a/hausser09a.pdf}}</description>
<link>https://academictorrents.com/download/f829b5170f398d4967cb73c21017e35d3f4ca234</link>
</item>
<item>
<title>Markov Properties for Linear Causal Models with Correlated Errors(Special Topic on Causality) (Paper)</title>
<description>@article{10:2,author={Changsung Kang and Jin Tian}, Title={Markov Properties for Linear Causal Models with Correlated Errors(Special Topic on Causality)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/kang09a/kang09a.pdf}}</description>
<link>https://academictorrents.com/download/91c569762ad4c2cee8abb9afab37b8e5cff93f6a</link>
</item>
<item>
<title>Maximum Entropy Discrimination Markov Networks (Paper)</title>
<description>@article{10:88,author={Jun Zhu and Eric P. Xing}, Title={Maximum Entropy Discrimination Markov Networks},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/zhu09a/zhu09a.pdf}}</description>
<link>https://academictorrents.com/download/a8bee85a0b8dcd390e3623c7db0a27f43222659b</link>
</item>
<item>
<title>On Efficient Large Margin Semisupervised Learning: Method and Theory (Paper)</title>
<description>@article{10:25,author={Junhui Wang and Xiaotong Shen and Wei Pan}, Title={On Efficient Large Margin Semisupervised Learning: Method and Theory},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/wang09a/wang09a.pdf}}</description>
<link>https://academictorrents.com/download/7d806d1b2f4361971692c59b3862d2bad741ce78</link>
</item>
<item>
<title>Concentration Bounds for Unigram Language Models (Paper)</title>
<description>@article{6:42,author={Evgeny Drukh and Yishay Mansour}, Title={Concentration Bounds for Unigram Language Models},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/drukh05a/drukh05a.pdf}}</description>
<link>https://academictorrents.com/download/c13eb0babbc742a76ffbddc13ff61cba5f32e8e5</link>
</item>
<item>
<title>Exact Simplification of Support Vector Solutions (Kernel Machines Section) (Paper)</title>
<description>@article{2:14,author={Tom Downs and Kevin E. Gates and Annette Masters}, Title={Exact Simplification of Support Vector Solutions
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/gentile01a/gentile01a.pdf}}</description>
<link>https://academictorrents.com/download/b0441320a6eeb7f16b4dbad0a762d48bbcbdb5cd</link>
</item>
<item>
<title>A Near-Optimal Algorithm for Differentially-Private Principal Components (Paper)</title>
<description>@article{14:90,author={Kamalika Chaudhuri and Anand D. Sarwate and Kaushik Sinha}, Title={A Near-Optimal Algorithm for Differentially-Private Principal Components},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/chaudhuri13a/chaudhuri13a.pdf}}</description>
<link>https://academictorrents.com/download/968f1af6a402ac653871675029d2bf9444d5d3aa</link>
</item>
<item>
<title>Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach (Paper)</title>
<description>@article{12:18,author={Gilles Meyer and Silvre Bonnabel and Rodolphe Sepulchre}, Title={Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/meyer11a/meyer11a.pdf}}</description>
<link>https://academictorrents.com/download/881e10e2a8109bc3c4c343e199fafdce1ba47e72</link>
</item>
<item>
<title>Statistical Consistency of Kernel Canonical Correlation Analysis (Paper)</title>
<description>@article{8:14,author={Kenji Fukumizu and Francis R. Bach and Arthur Gretton}, Title={Statistical Consistency of Kernel Canonical Correlation Analysis},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/fukumizu07a/fukumizu07a.pdf}}</description>
<link>https://academictorrents.com/download/addad9344cac172f38057eac4c68c2dc19ae1813</link>
</item>
<item>
<title>Kernel Analysis of Deep Networks (Paper)</title>
<description>@article{12:78,author={Grgoire Montavon and Mikio L. Braun and Klaus-Robert Mller}, Title={Kernel Analysis of Deep Networks},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/montavon11a/montavon11a.pdf}}</description>
<link>https://academictorrents.com/download/5975549bc2e7cc159af7906844199fc8ac59c425</link>
</item>
<item>
<title>Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation (Paper)</title>
<description>@article{7:22,author={Kazuho Watanabe and Sumio Watanabe}, Title={Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/watanabe06a/watanabe06a.pdf}}</description>
<link>https://academictorrents.com/download/987a4a59d5e3361d945c18d53ccc12beabb05598</link>
</item>
<item>
<title>Two Distributed-State Models For Generating High-Dimensional Time Series (Paper)</title>
<description>@article{12:28,author={Graham W. Taylor and Geoffrey E. Hinton and Sam T. Roweis}, Title={Two Distributed-State Models For Generating High-Dimensional Time Series},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/taylor11a/taylor11a.pdf}}</description>
<link>https://academictorrents.com/download/d044760b9925a1eb2861475316f5938a6141a2ef</link>
</item>
<item>
<title>Unlabeled Compression Schemes for Maximum Classes (Paper)</title>
<description>@article{8:70,author={Dima Kuzmin and Manfred K. Warmuth}, Title={Unlabeled Compression Schemes for Maximum Classes},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/kuzmin07a/kuzmin07a.pdf}}</description>
<link>https://academictorrents.com/download/f939f605369d0dc11ec8ccaf6f472911be5c56d3</link>
</item>
<item>
<title>The Stationary Subspace Analysis Toolbox (Paper)</title>
<description>@article{12:93,author={Jan Saputra Mller and Paul von Bnau and Frank C. Meinecke and Franz J. Kirly and Klaus-Robert Mller}, Title={The Stationary Subspace Analysis Toolbox},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/mueller11a/mueller11a.pdf}}</description>
<link>https://academictorrents.com/download/f3210a6851c50793e0b96e20867b5c717cf6d489</link>
</item>
<item>
<title>The Locally Weighted Bag of Words Framework for Document Representation (Paper)</title>
<description>@article{8:80,author={Guy Lebanon and Yi Mao and Joshua Dillon}, Title={The Locally Weighted Bag of Words Framework for Document Representation},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/lebanon07a/lebanon07a.pdf}}</description>
<link>https://academictorrents.com/download/b0f8ae49890d4cf83f1f0d2deb274725dba71e32</link>
</item>
<item>
<title>Characterizing the Function Space for Bayesian Kernel Models (Paper)</title>
<description>@article{8:62,author={Natesh S. Pillai and Qiang Wu and Feng Liang and Sayan Mukherjee and Robert L. Wolpert}, Title={Characterizing the Function Space for Bayesian Kernel Models},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/pillai07a/pillai07a.pdf}}</description>
<link>https://academictorrents.com/download/1ea04a9b83c5a3e90ca3b6ec6db8fd330d6547c6</link>
</item>
<item>
<title>On Equivalence Relationships Between Classification and Ranking Algorithms (Paper)</title>
<description>@article{12:89,author={eyda Ertekin and Cynthia Rudin}, Title={On Equivalence Relationships Between Classification and Ranking Algorithms},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/ertekin11a/ertekin11a.pdf}}</description>
<link>https://academictorrents.com/download/67298f2c214fabd83eab7dd209d6c010e91e040a</link>
</item>
<item>
<title>Recommender Systems Using Linear Classifiers (Paper)</title>
<description>@article{2:16,author={Tong Zhang and Vijay S. Iyengar}, Title={Recommender Systems Using Linear Classifiers},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/crammer01a/crammer01a.pdf}}</description>
<link>https://academictorrents.com/download/540a23376d40d553340e7b44a2ce99efac5215d7</link>
</item>
<item>
<title>One-Class SVMs for Document Classification (Kernel Machines Section) (Paper)</title>
<description>@article{2:8,author={Larry M. Manevitz and Malik Yousef}, Title={One-Class SVMs for Document Classification
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/horn01a/rev1/horn01ar1.pdf}}</description>
<link>https://academictorrents.com/download/8bb752460e3ddecdf5c6e97aef7c4f6300429f35</link>
</item>
<item>
<title>Efficient and Effective Visual Codebook Generation Using Additive Kernels (Paper)</title>
<description>@article{12:95,author={Jianxin Wu and Wei-Chian Tan and James M. Rehg}, Title={Efficient and Effective Visual Codebook Generation Using Additive Kernels},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/wu11b/wu11b.pdf}}</description>
<link>https://academictorrents.com/download/7b4b3bbdc638189b68335bfbea6a25ba5fddd0bc</link>
</item>
<item>
<title>One-Class Novelty Detection for Seizure Analysis from Intracranial EEG (Paper)</title>
<description>@article{7:37,author={Andrew B. Gardner and Abba M. Krieger and George Vachtsevanos and Brian Litt}, Title={One-Class Novelty Detection for Seizure Analysis from Intracranial EEG},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/gardner06a/gardner06a.pdf}}</description>
<link>https://academictorrents.com/download/82f2ad2030b66cdb205fa6726dfcb13413963ae0</link>
</item>
<item>
<title>Weisfeiler-Lehman Graph Kernels (Paper)</title>
<description>@article{12:77,author={Nino Shervashidze and Pascal Schweitzer and Erik Jan van Leeuwen and Kurt Mehlhorn and Karsten M. Borgwardt}, Title={Weisfeiler-Lehman Graph Kernels},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/shervashidze11a/shervashidze11a.pdf}}</description>
<link>https://academictorrents.com/download/cd8ab06b42bef8ed5f3f2fdff35e91caad1ee17f</link>
</item>
<item>
<title>Machine Learning for Computer Security(Special Topic on Machine Learning for Computer Security) (Paper)</title>
<description>@article{7:96,author={Philip K. Chan and Richard P. Lippmann}, Title={Machine Learning for Computer Security(Special Topic on Machine Learning for Computer Security)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/MLSEC-intro06a/MLSEC-intro06a.pdf}}</description>
<link>https://academictorrents.com/download/3eced34cd948e7ea92f31ded3e0fd734274fee4a</link>
</item>
<item>
<title>Provably Efficient Learning with Typed Parametric Models (Paper)</title>
<description>@article{10:68,author={Emma Brunskill and Bethany R. Leffler and Lihong Li and Michael L. Littman and Nicholas Roy}, Title={Provably Efficient Learning with Typed Parametric Models},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/brunskill09a/brunskill09a.pdf}}</description>
<link>https://academictorrents.com/download/aaa2d79273c5aa9e6e5a3d88d5b23f31b1b42884</link>
</item>
<item>
<title>Nonparametric Quantile Estimation (Paper)</title>
<description>@article{7:45,author={Ichiro Takeuchi and Quoc V. Le and Timothy D. Sears and Alexander J. Smola}, Title={Nonparametric Quantile Estimation},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/takeuchi06a/takeuchi06a.pdf}}</description>
<link>https://academictorrents.com/download/8cbd0b0a61916754b4346403670e203f8712437b</link>
</item>
<item>
<title>Fast Iterative Kernel Principal Component Analysis (Paper)</title>
<description>@article{8:66,author={Simon Gnter and Nicol N. Schraudolph and S. V. N. Vishwanathan}, Title={Fast Iterative Kernel Principal Component Analysis},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/guenter07a/guenter07a.pdf}}</description>
<link>https://academictorrents.com/download/a63f5649a1288237c6f5d3904c1999a8c889ab90</link>
</item>
<item>
<title>Support Vector Clustering (Kernel Machines Section) (Paper)</title>
<description>@article{2:7,author={Asa Ben-Hur and David Horn and Hava T. Siegelmann and Vladimir Vapnik}, Title={Support Vector Clustering
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/horn01a/horn01a.pdf}}</description>
<link>https://academictorrents.com/download/676311e19d559eff4c0d65ed23abb7fd143a03d3</link>
</item>
<item>
<title>Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks (Paper)</title>
<description>@article{10:52,author={Nikolai Slobodianik and Dmitry Zaporozhets and Neal Madras}, Title={Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/slobodianik09a/slobodianik09a.pdf}}</description>
<link>https://academictorrents.com/download/86baaa1e04744d1efdd63b5627f0cde988b82aae</link>
</item>
<item>
<title>AdaBoost is Consistent (Paper)</title>
<description>@article{8:78,author={Peter L. Bartlett and Mikhail Traskin}, Title={AdaBoost is Consistent},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/bartlett07b/bartlett07b.pdf}}</description>
<link>https://academictorrents.com/download/0eb2fb76eb57bfb05278dcecc6a6b2a297d65dfd</link>
</item>
<item>
<title>Clustering with Bregman Divergences (Paper)</title>
<description>@article{6:58,author={Arindam Banerjee and Srujana Merugu and Inderjit S. Dhillon and Joydeep Ghosh}, Title={Clustering with Bregman Divergences},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/banerjee05b/banerjee05b.pdf}}</description>
<link>https://academictorrents.com/download/db239d92ba00c26cab551146d29f9ce404de4206</link>
</item>
<item>
<title>Adaptive Exact Inference in Graphical Models (Paper)</title>
<description>@article{12:97,author={zgr Smer and Umut A. Acar and Alexander T. Ihler and Ramgopal R. Mettu}, Title={Adaptive Exact Inference in Graphical Models},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/sumer11a/sumer11a.pdf}}</description>
<link>https://academictorrents.com/download/ad910eb28d6a12ce53198743901eb3aac2d6f3d2</link>
</item>
<item>
<title>Better Algorithms for Benign Bandits (Paper)</title>
<description>@article{12:35,author={Elad Hazan and Satyen Kale}, Title={Better Algorithms for Benign Bandits},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/hazan11a/hazan11a.pdf}}</description>
<link>https://academictorrents.com/download/aafc09fd9ad321c97aa4a22a5fe9430375c7323e</link>
</item>
<item>
<title>Machine Learning with Data Dependent Hypothesis Classes (Paper)</title>
<description>@article{2:17,author={Adam Cannon and J. Mark Ettinger and Don Hush and Clint Scovel}, Title={Machine Learning with Data Dependent Hypothesis Classes},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/downs01a/downs01a.pdf}}</description>
<link>https://academictorrents.com/download/6ad9ad36edba1929e716dd78101cd00e3fbf6376</link>
</item>
<item>
<title>Learning Spectral Clustering, With Application To Speech Separation (Paper)</title>
<description>@article{7:71,author={Francis R. Bach and Michael I. Jordan}, Title={Learning Spectral Clustering, With Application To Speech Separation},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/bach06b/bach06b.pdf}}</description>
<link>https://academictorrents.com/download/ceb026d186ec8a3a36151226d8bf3b939d313018</link>
</item>
<item>
<title>Incremental Algorithms for Hierarchical Classification (Paper)</title>
<description>@article{7:2,author={Nicol Cesa-Bianchi and Claudio Gentile and Luca Zaniboni}, Title={Incremental Algorithms for Hierarchical Classification},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/cesa-bianchi06a/cesa-bianchi06a.pdf}}</description>
<link>https://academictorrents.com/download/8e9fbb3ea861d404bb3ad130f2aa9c970ecc4778</link>
</item>
<item>
<title>Linear State-Space Models for Blind Source Separation (Paper)</title>
<description>@article{7:92,author={Rasmus Kongsgaard Olsson and Lars Kai Hansen}, Title={Linear State-Space Models for Blind Source Separation},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/olsson06a/olsson06a.pdf}}</description>
<link>https://academictorrents.com/download/1d93e447c4599e8d41257cb05574b9fc81b2d1ce</link>
</item>
<item>
<title>Bilinear Discriminant Component Analysis (Paper)</title>
<description>@article{8:39,author={Mads Dyrholm and Christoforos Christoforou and Lucas C. Parra}, Title={Bilinear Discriminant Component Analysis},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/dyrholm07a/dyrholm07a.pdf}}</description>
<link>https://academictorrents.com/download/4a632373b4f3d101d2991d50a06c30b28f58c9b9</link>
</item>
<item>
<title>Structure Spaces (Paper)</title>
<description>@article{10:93,author={Brijnesh J. Jain and Klaus Obermayer}, Title={Structure Spaces},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/jain09a/jain09a.pdf}}</description>
<link>https://academictorrents.com/download/61cf92536eea9604d8755af5ee0e712e718f0692</link>
</item>
<item>
<title>Model Monitor (M2): Evaluating, Comparing, and Monitoring Models(Machine Learning Open Source Software Paper) (Paper)</title>
<description>@article{10:47,author={Troy Raeder and Nitesh V. Chawla}, Title={Model Monitor (M2): Evaluating, Comparing, and Monitoring Models(Machine Learning Open Source Software Paper)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/raeder09a/raeder09a.pdf}}</description>
<link>https://academictorrents.com/download/537f6b864879a1141c725ccdb56b78efe10bb5d7</link>
</item>
<item>
<title>Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:51,author={Matthias Heiler and Christoph Schnrr}, Title={Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/heiler06a/heiler06a.pdf}}</description>
<link>https://academictorrents.com/download/b430633dd781b7b7357597c66666f9ea73b7be0a</link>
</item>
<item>
<title>Universal Kernels (Paper)</title>
<description>@article{7:95,author={Charles A. Micchelli and Yuesheng Xu and Haizhang Zhang}, Title={Universal Kernels},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/micchelli06a/micchelli06a.pdf}}</description>
<link>https://academictorrents.com/download/966332ffa949ca565e874c3b0c4db39b3bddf2b4</link>
</item>
<item>
<title>Semigroup Kernels on Measures (Paper)</title>
<description>@article{6:40,author={Marco Cuturi and Kenji Fukumizu and Jean-Philippe Vert}, Title={Semigroup Kernels on Measures},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/cuturi05a/cuturi05a.pdf}}</description>
<link>https://academictorrents.com/download/50c2c345aacf27cf1065fc09716eb8259b3dcc51</link>
</item>
<item>
<title>Considering Cost Asymmetry in Learning Classifiers (Paper)</title>
<description>@article{7:63,author={Francis R. Bach and David Heckerman and Eric Horvitz}, Title={Considering Cost Asymmetry in Learning Classifiers},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/bach06a/bach06a.pdf}}</description>
<link>https://academictorrents.com/download/a9bd8db4e6ddb322259cacaa0c0668f7b6fc1290</link>
</item>
<item>
<title>Faster Algorithms for Max-Product Message-Passing (Paper)</title>
<description>@article{12:37,author={Julian J. McAuley and Tibrio S. Caetano}, Title={Faster Algorithms for Max-Product Message-Passing},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/mcauley11a/mcauley11a.pdf}}</description>
<link>https://academictorrents.com/download/21b53d95668aa4a95bdde21e4cde68ff9b6d6803</link>
</item>
<item>
<title>The Need for Open Source Software in Machine Learning (Paper)</title>
<description>@article{8:81,author={Sren Sonnenburg and Mikio L. Braun and Cheng Soon Ong and Samy Bengio and Leon Bottou and Geoffrey Holmes and Yann LeCun and Klaus-Robert Mller and Fernando Pereira and Carl Edward Rasmussen and Gunnar Rtsch and Bernhard Schlkopf and Alexander Smola and Pascal Vincent and Jason Weston and Robert Williamson}, Title={The Need for Open Source Software in Machine Learning},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/sonnenburg07a/sonnenburg07a.pdf}}</description>
<link>https://academictorrents.com/download/c859121d01e0ae57fd36488c9a5c529b29815685</link>
</item>
<item>
<title>Variable Sparsity Kernel Learning (Paper)</title>
<description>@article{12:17,author={Jonathan Aflalo and Aharon Ben-Tal and Chiranjib Bhattacharyya and Jagarlapudi Saketha Nath and Sankaran Raman}, Title={Variable Sparsity Kernel Learning},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/aflalo11a/aflalo11a.pdf}}</description>
<link>https://academictorrents.com/download/270fcf02619a09a2adc72c279551898d2e7ae330</link>
</item>
<item>
<title>Margin-based Ranking and an Equivalence between AdaBoost and RankBoost (Paper)</title>
<description>@article{10:77,author={Cynthia Rudin and Robert E. Schapire}, Title={Margin-based Ranking and an Equivalence between AdaBoost and RankBoost},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/rudin09a/rudin09a.pdf}}</description>
<link>https://academictorrents.com/download/6cd0a8f32126a69e15a2cd64214f78ba1d794552</link>
</item>
<item>
<title>High-dimensional Covariance Estimation Based On Gaussian Graphical Models (Paper)</title>
<description>@article{12:91,author={Shuheng Zhou and Philipp Rtimann and Min Xu and Peter Bhlmann}, Title={High-dimensional Covariance Estimation Based On Gaussian Graphical Models},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/zhou11a/zhou11a.pdf}}</description>
<link>https://academictorrents.com/download/b3cd62da6ee16228b9c1065111e49e299d3ab1ae</link>
</item>
<item>
<title>The Sample Complexity of Dictionary Learning (Paper)</title>
<description>@article{12:100,author={Daniel Vainsencher and Shie Mannor and Alfred M. Bruckstein}, Title={The Sample Complexity of Dictionary Learning},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/vainsencher11a/vainsencher11a.pdf}}</description>
<link>https://academictorrents.com/download/05ff4e0edd719b5f4665f8f2b4bda672882e6496</link>
</item>
<item>
<title>Models of Cooperative Teaching and Learning (Paper)</title>
<description>@article{12:11,author={Sandra Zilles and Steffen Lange and Robert Holte and Martin Zinkevich}, Title={Models of Cooperative Teaching and Learning},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/zilles11a/zilles11a.pdf}}</description>
<link>https://academictorrents.com/download/8c3255d6bad6b7202880db1517458f28afe55254</link>
</item>
<item>
<title>Maximum Margin Algorithms with Boolean Kernels (Paper)</title>
<description>@article{6:48,author={Roni Khardon and Rocco A. Servedio}, Title={Maximum Margin Algorithms with Boolean Kernels},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/khardon05a/khardon05a.pdf}}</description>
<link>https://academictorrents.com/download/7dc07f4ef25da9cd9da379f80e6f88873b9fc6ba</link>
</item>
<item>
<title>Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss (Paper)</title>
<description>@article{7:86,author={Di-Rong Chen and Tao Sun}, Title={Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/chen06a/chen06a.pdf}}</description>
<link>https://academictorrents.com/download/d69e949c56375d58ede4a94869d91f971e22c59e</link>
</item>
<item>
<title>Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts (Paper)</title>
<description>@article{8:91,author={J. Zico Kolter and Marcus A. Maloof}, Title={Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/kolter07a/kolter07a.pdf}}</description>
<link>https://academictorrents.com/download/682e6eabe24d0007d9b35df1490e66f211379437</link>
</item>
<item>
<title>An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems (Paper)</title>
<description>@article{10:72,author={Luciana Ferrer and Kemal Snmez and Elizabeth Shriberg}, Title={An Anticorrelation Kernel for Subsystem Training in Multiple Classifier Systems},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/ferrer09a/ferrer09a.pdf}}</description>
<link>https://academictorrents.com/download/c684ecfe5efb99fd7b4dfeba356e761dbe7d9821</link>
</item>
<item>
<title>Learning Recursive Control Programs from Problem Solving (Special Topic on Inductive Programming) (Paper)</title>
<description>@article{7:17,author={Pat Langley and Dongkyu Choi}, Title={Learning Recursive Control Programs from Problem Solving (Special Topic on Inductive Programming)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/langley06a/langley06a.pdf}}</description>
<link>https://academictorrents.com/download/df4cf8ef981a46e6b2307e3d80c7134676a3edd2</link>
</item>
<item>
<title>Graph Laplacians and their Convergence on Random Neighborhood Graphs (Special Topic on the Conference on Learning Theory 2005) (Paper)</title>
<description>@article{8:48,author={Matthias Hein and Jean-Yves Audibert and Ulrike von Luxburg}, Title={Graph Laplacians and their Convergence on Random Neighborhood Graphs (Special Topic on the Conference on Learning Theory 2005)},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/hein07a/hein07a.pdf}}</description>
<link>https://academictorrents.com/download/b97150c05da979971d5f724fb88b14ab703d4b35</link>
</item>
<item>
<title>Stability and Generalization (Paper)</title>
<description>@article{2:22,author={Olivier Bousquet and Andr Elisseeff}, Title={Stability and Generalization},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/meek02a/meek02a.pdf}}</description>
<link>https://academictorrents.com/download/1c04354e3b6a4368ec412fd54de9a8e4d59ed30a</link>
</item>
<item>
<title>A Nonparametric Statistical Approach to Clustering via Mode Identification (Paper)</title>
<description>@article{8:59,author={Jia Li and Surajit Ray and Bruce G. Lindsay}, Title={A Nonparametric Statistical Approach to Clustering via Mode Identification},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/li07a/li07a.pdf}}</description>
<link>https://academictorrents.com/download/076359232032191f2a858982ff463ddfe08b4ea9</link>
</item>
<item>
<title>Text Classification using String Kernels (Paper)</title>
<description>@article{2:20,author={Huma Lodhi and Craig Saunders and John Shawe-Taylor and Nello Cristianini and Chris Watkins}, Title={Text Classification using String Kernels},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/cannon02a/cannon02a.pdf}}</description>
<link>https://academictorrents.com/download/4c9c48699f20f3b4b62e59ca927fef79cebdf5e0</link>
</item>
<item>
<title>Communication-Efficient Algorithms for Statistical Optimization (Paper)</title>
<description>@article{14:105,author={Yuchen Zhang and John C. Duchi and Martin J. Wainwright}, Title={Communication-Efficient Algorithms for Statistical Optimization},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/zhang13b/zhang13b.pdf}}</description>
<link>https://academictorrents.com/download/0710d4361d4ca489c7ca6e500ccce71d7699ebe3</link>
</item>
<item>
<title>Polynomial-Delay Enumeration of Monotonic Graph Classes (Paper)</title>
<description>@article{10:33,author={Jan Ramon and Siegfried Nijssen}, Title={Polynomial-Delay Enumeration of Monotonic Graph Classes},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/ramon09a/ramon09a.pdf}}</description>
<link>https://academictorrents.com/download/0220f37a4a1cdf8b936d67cb94c4176e66502efa</link>
</item>
<item>
<title>Learning Coordinate Covariances via Gradients (Paper)</title>
<description>@article{7:18,author={Sayan Mukherjee and Ding-Xuan Zhou}, Title={Learning Coordinate Covariances via Gradients},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/mukherjee06a/mukherjee06a.pdf}}</description>
<link>https://academictorrents.com/download/456bd90c7337fe6f76a6e2fce0374d41b5f46cbe</link>
</item>
<item>
<title>Operator Norm Convergence of Spectral Clustering on Level Sets (Paper)</title>
<description>@article{12:12,author={Bruno Pelletier and Pierre Pudlo}, Title={Operator Norm Convergence of Spectral Clustering on Level Sets},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/pelletier11a/pelletier11a.pdf}}</description>
<link>https://academictorrents.com/download/fc7d3a5d7832e95df39a5f5b15ce5f507c7771fa</link>
</item>
<item>
<title>Asymptotic Model Selection for Naive Bayesian Networks (Paper)</title>
<description>@article{6:1,author={Dmitry Rusakov and Dan Geiger}, Title={Asymptotic Model Selection for Naive Bayesian Networks},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/rusakov05a/rusakov05a.pdf}}</description>
<link>https://academictorrents.com/download/6690e0941328f9ea8724754e91f0e73a25c85b3e</link>
</item>
<item>
<title>Spam Filtering Based On The Analysis Of Text Information Embedded Into Images (Special Topic on Machine Learning for Computer Security) (Paper)</title>
<description>@article{7:98,author={Giorgio Fumera and Ignazio Pillai and Fabio Roli}, Title={Spam Filtering Based On The Analysis Of Text Information Embedded Into Images
(Special Topic on Machine Learning for Computer Security)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/fumera06a/fumera06a.pdf}}</description>
<link>https://academictorrents.com/download/ad9c48d261d03045f96d3202cf4858fc57b20699</link>
</item>
<item>
<title>A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning (Special Topic on Model Selection) (Paper)</title>
<description>@article{8:90,author={Carine Hue and Marc Boull}, Title={A New Probabilistic Approach in Rank Regression with Optimal Bayesian Partitioning (Special Topic on Model Selection)},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/hue07a/hue07a.pdf}}</description>
<link>https://academictorrents.com/download/c7d9b7be3f7aaa9167cae52a23e800c0e0ef5cea</link>
</item>
<item>
<title>A Direct Method for Building Sparse Kernel Learning Algorithms (Paper)</title>
<description>@article{7:21,author={Mingrui Wu and Bernhard Schlkopf and Gkhan Bakr}, Title={A Direct Method for Building Sparse Kernel Learning Algorithms},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/wu06a/wu06a.pdf}}</description>
<link>https://academictorrents.com/download/2e96deb9f1a0b27ff0c3a2fd6d89b83fa47672aa</link>
</item>
<item>
<title>Learning the Kernel Function via Regularization (Paper)</title>
<description>@article{6:38,author={Charles A. Micchelli and Massimiliano Pontil}, Title={Learning the Kernel Function via Regularization},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/micchelli05a/micchelli05a.pdf}}</description>
<link>https://academictorrents.com/download/6bf074135eaaea73a3e5d6e548b0b31333ab906e</link>
</item>
<item>
<title>Diffusion Kernels on Statistical Manifolds (Paper)</title>
<description>@article{6:5,author={John Lafferty and Guy Lebanon}, Title={Diffusion Kernels on Statistical Manifolds},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/lafferty05a/lafferty05a.pdf}}</description>
<link>https://academictorrents.com/download/4209420e28a999fa898e2305185feb3728d272e9</link>
</item>
<item>
<title>Adaptive Subgradient Methods for Online Learning and Stochastic Optimization (Paper)</title>
<description>@article{12:61,author={John Duchi and Elad Hazan and Yoram Singer}, Title={Adaptive Subgradient Methods for Online Learning and Stochastic Optimization},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf}}</description>
<link>https://academictorrents.com/download/d385e01673b699db102a3a362ebb4fba46ee3660</link>
</item>
<item>
<title>Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization (Paper)</title>
<description>@article{10:76,author={Vojtch Franc and Sren Sonnenburg}, Title={Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/franc09a/franc09a.pdf}}</description>
<link>https://academictorrents.com/download/e0851617cd76e1cfa4de188b713af44ac40816fc</link>
</item>
<item>
<title>Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space (Kernel Machines Section) (Paper)</title>
<description>@article{2:6,author={Roman Rosipal and Leonard J. Trejo}, Title={Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/rosipal01a/rosipal01a.pdf}}</description>
<link>https://academictorrents.com/download/36e0378030715a494a927af440defed23b2880bf</link>
</item>
<item>
<title>Union Support Recovery in Multi-task Learning (Paper)</title>
<description>@article{12:72,author={Mladen Kolar and John Lafferty and Larry Wasserman}, Title={Union Support Recovery in Multi-task Learning},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/kolar11a/kolar11a.pdf}}</description>
<link>https://academictorrents.com/download/d5ec14d9c059a661009328f8e42a8fb8114a72ae</link>
</item>
<item>
<title>Some Theory for Generalized Boosting Algorithms (Paper)</title>
<description>@article{7:25,author={Peter J. Bickel and Ya'acov Ritov and Alon Zakai}, Title={Some Theory for Generalized Boosting Algorithms},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/bickel06a/bickel06a.pdf}}</description>
<link>https://academictorrents.com/download/8ddc6ba461fcd7847186f19b86033644dfaa61ec</link>
</item>
<item>
<title>Bayesian Co-Training (Paper)</title>
<description>@article{12:80,author={Shipeng Yu and Balaji Krishnapuram and Rmer Rosales and R. Bharat Rao}, Title={Bayesian Co-Training},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/yu11a/yu11a.pdf}}</description>
<link>https://academictorrents.com/download/87746816742b37abaaaa19d5fe915269d46c2c27</link>
</item>
<item>
<title>Clustering on the Unit Hypersphere using von Mises-Fisher Distributions (Paper)</title>
<description>@article{6:46,author={Arindam Banerjee and Inderjit S. Dhillon and Joydeep Ghosh and Suvrit Sra}, Title={Clustering on the Unit Hypersphere using von Mises-Fisher  Distributions},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/banerjee05a/banerjee05a.pdf}}</description>
<link>https://academictorrents.com/download/89d043438b621c67d89d46dcca3275739c0ac799</link>
</item>
<item>
<title>Estimating Functions for Blind Separation When Sources Have Variance Dependencies (Paper)</title>
<description>@article{6:16,author={Motoaki Kawanabe and Klaus-Robert Mller}, Title={Estimating Functions for Blind Separation When Sources Have Variance Dependencies},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/kawanabe05a/kawanabe05a.pdf}}</description>
<link>https://academictorrents.com/download/00fcc670016f036c2ff9f3df3c0d3437356e0588</link>
</item>
<item>
<title>Evolutionary Model Type Selection for Global Surrogate Modeling (Paper)</title>
<description>@article{10:71,author={Dirk Gorissen and Tom Dhaene and Filip De Turck}, Title={Evolutionary Model Type Selection for Global Surrogate Modeling},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/gorissen09a/gorissen09a.pdf}}</description>
<link>https://academictorrents.com/download/2dc016a42d36912d559e9896404a8afd43ccf47e</link>
</item>
<item>
<title>A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis (Paper)</title>
<description>@article{7:42,author={Enrique Castillo and Bertha Guijarro-Berdias and Oscar Fontenla-Romero and Amparo Alonso-Betanzos}, Title={A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/castillo06a/castillo06a.pdf}}</description>
<link>https://academictorrents.com/download/2b2586a299e191fa8329d6ca35f1892d174071f4</link>
</item>
<item>
<title>A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n (Paper)</title>
<description>@article{7:94,author={Robert Castelo and Alberto Roverato}, Title={A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/castelo06a/castelo06a.pdf}}</description>
<link>https://academictorrents.com/download/c782c5a3a2161449b7b4c7aa9cb57dab69887027</link>
</item>
<item>
<title>Incorporating Functional Knowledge in Neural Networks (Paper)</title>
<description>@article{10:42,author={Charles Dugas and Yoshua Bengio and Franois Blisle and Claude Nadeau and Ren Garcia}, Title={Incorporating Functional Knowledge in Neural Networks},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/dugas09a/dugas09a.pdf}}</description>
<link>https://academictorrents.com/download/f52efac637894c976728119495b979790e2b894e</link>
</item>
<item>
<title>Learning to Detect and Classify Malicious Executables in the Wild (Special Topic on Machine Learning for Computer Security) (Paper)</title>
<description>@article{7:99,author={J. Zico Kolter and Marcus A. Maloof}, Title={Learning to Detect and Classify Malicious Executables in the Wild
(Special Topic on Machine Learning for Computer Security)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/kolter06a/kolter06a.pdf}}</description>
<link>https://academictorrents.com/download/ab04cea5def5dc64eb39a5ace8f958aeff955c13</link>
</item>
<item>
<title>Computational and Theoretical Analysis of Null Space and Orthogonal Linear Discriminant Analysis (Paper)</title>
<description>@article{7:43,author={Jieping Ye and Tao Xiong}, Title={Computational and Theoretical Analysis of  Null Space  and Orthogonal Linear Discriminant Analysis},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/ye06a/ye06a.pdf}}</description>
<link>https://academictorrents.com/download/006d1a3aac3e2ac9ac996592e94287fe22a8dd7d</link>
</item>
<item>
<title>Boosted Classification Trees and Class ProbabilityQuantile Estimation (Paper)</title>
<description>@article{8:16,author={David Mease and Abraham J. Wyner and Andreas Buja}, Title={Boosted Classification Trees and Class ProbabilityQuantile Estimation},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/mease07a/mease07a.pdf}}</description>
<link>https://academictorrents.com/download/5b16ef1142ebaa902bf0b2c0e0fc5c3b5793f1ca</link>
</item>
<item>
<title>Hierarchical Knowledge Gradient for Sequential Sampling (Paper)</title>
<description>@article{12:90,author={Martijn R.K. Mes and Warren B. Powell and Peter I. Frazier}, Title={Hierarchical Knowledge Gradient for Sequential Sampling},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/mes11a/mes11a.pdf}}</description>
<link>https://academictorrents.com/download/8fa61f0a8f3d7f90f077633c6519f90a112e157f</link>
</item>
<item>
<title>Subgroup Analysis via Recursive Partitioning (Paper)</title>
<description>@article{10:5,author={Xiaogang Su and Chih-Ling Tsai and Hansheng Wang and David M. Nickerson and Bogong Li}, Title={Subgroup Analysis via Recursive Partitioning},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/su09a/su09a.pdf}}</description>
<link>https://academictorrents.com/download/a5a7097ea1e9a4f1eadfbaafb1c59c9ef579d0d9</link>
</item>
<item>
<title>A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis (Paper)</title>
<description>@article{12:58,author={Trine Julie Abrahamsen and Lars Kai Hansen}, Title={A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/abrahamsen11a/abrahamsen11a.pdf}}</description>
<link>https://academictorrents.com/download/43e3d8cc504a81b9f2d5169a4cdcd6b782e66c0f</link>
</item>
<item>
<title>General Polynomial Time Decomposition Algorithms (Special Topic on the Conference on Learning Theory 2005) (Paper)</title>
<description>@article{8:12,author={Nikolas List and Hans Ulrich Simon}, Title={General Polynomial Time Decomposition Algorithms (Special Topic on the Conference on Learning Theory 2005)},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/list07a/list07a.pdf}}</description>
<link>https://academictorrents.com/download/85cad139e5d7933d2a0a9609042d7f65d5234910</link>
</item>
<item>
<title>Introduction to the Special Issue on Kernel Methods (Kernel Machines Section) (Paper)</title>
<description>@article{2:5,author={Nello Cristianini and John Shawe-Taylor and Robert C. Williamson}, Title={Introduction to the Special Issue on Kernel Methods
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/cristianini01a/cristianini01a.pdf}}</description>
<link>https://academictorrents.com/download/d16ac871220cb7c33415ffaa77bdd94309cb7b3c</link>
</item>
<item>
<title>A Bayesian Approximation Method for Online Ranking (Paper)</title>
<description>@article{12:9,author={Ruby C. Weng and Chih-Jen Lin}, Title={A Bayesian Approximation Method for Online Ranking},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/weng11a/weng11a.pdf}}</description>
<link>https://academictorrents.com/download/61f3fcb50a5e10fd40d461042e9bc2d33e263e9f</link>
</item>
<item>
<title>SparseRobust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory (Paper)</title>
<description>@article{14:83,author={Aleksandr Y. Aravkin and James V. Burke and Gianluigi Pillonetto}, Title={SparseRobust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/aravkin13a/aravkin13a.pdf}}</description>
<link>https://academictorrents.com/download/4f11055a66aee0bc992534acd0a0bfad33a284d0</link>
</item>
<item>
<title>Bayesian Network Learning with Parameter Constraints (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:50,author={Radu Stefan Niculescu and Tom M. Mitchell and R. Bharat Rao}, Title={Bayesian Network Learning with Parameter Constraints (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/niculescu06a/niculescu06a.pdf}}</description>
<link>https://academictorrents.com/download/56d216a606fa1fcf0e1338b7f6252889e7ec351a</link>
</item>
<item>
<title>Robust Gaussian Process Regression with a Student-t Likelihood (Paper)</title>
<description>@article{12:99,author={Pasi Jylnki and Jarno Vanhatalo and Aki Vehtari}, Title={Robust Gaussian Process Regression with a Student-t Likelihood},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/jylanki11a/jylanki11a.pdf}}</description>
<link>https://academictorrents.com/download/e49fb903a9419d945066f323bd1a6785eeb8f55c</link>
</item>
<item>
<title>Shallow Parsing using Specialized HMMs (Paper)</title>
<description>@article{2:26,author={Antonio Molina and Ferran Pla}, Title={Shallow Parsing using Specialized HMMs},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/bousquet02a/bousquet02a.pdf}}</description>
<link>https://academictorrents.com/download/2d7636b11b729d5c096b0195125f45d396d17314</link>
</item>
<item>
<title>Handling Missing Values when Applying Classification Models (Paper)</title>
<description>@article{8:57,author={Maytal Saar-Tsechansky and Foster Provost}, Title={Handling Missing Values when Applying Classification Models},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/saar-tsechansky07a/saar-tsechansky07a.pdf}}</description>
<link>https://academictorrents.com/download/2c4f83e3f6f4e0468ac16780caedd93c5799e263</link>
</item>
<item>
<title>Forest Density Estimation (Paper)</title>
<description>@article{12:25,author={Han Liu and Min Xu and Haijie Gu and Anupam Gupta and John Lafferty and Larry Wasserman}, Title={Forest Density Estimation},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/liu11a/liu11a.pdf}}</description>
<link>https://academictorrents.com/download/1d44d4af516810eca11fdeb7430b1cafc6971c59</link>
</item>
<item>
<title>New Horn Revision Algorithms (Paper)</title>
<description>@article{6:64,author={Judy Goldsmith and Robert H. Sloan}, Title={New Horn Revision Algorithms},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/goldsmith05a/goldsmith05a.pdf}}</description>
<link>https://academictorrents.com/download/38e420740a5236e66eda4757734adb1380289565</link>
</item>
<item>
<title>On Inferring Application Protocol Behaviors in Encrypted Network Traffic (Special Topic on Machine Learning for Computer Security) (Paper)</title>
<description>@article{7:100,author={Charles V. Wright and Fabian Monrose and Gerald M. Masson}, Title={On Inferring Application Protocol Behaviors in Encrypted Network Traffic
(Special Topic on Machine Learning for Computer Security)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/wright06a/wright06a.pdf}}</description>
<link>https://academictorrents.com/download/fd60073fa47e2fe363b49d64545720cac67f2797</link>
</item>
<item>
<title>Estimation of Gradients and Coordinate Covariation in Classification (Paper)</title>
<description>@article{7:88,author={Sayan Mukherjee and Qiang Wu}, Title={Estimation of Gradients and Coordinate Covariation in Classification},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/mukherjee06b/mukherjee06b.pdf}}</description>
<link>https://academictorrents.com/download/aca9aaca4f8f1084ea4f140ffdb2f1c44b05bcdc</link>
</item>
<item>
<title>From External to Internal Regret (Special Topic on the Conference on Learning Theory 2005) (Paper)</title>
<description>@article{8:47,author={Avrim Blum and Yishay Mansour}, Title={From External to Internal Regret (Special Topic on the Conference on Learning Theory 2005)},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/blum07a/blum07a.pdf}}</description>
<link>https://academictorrents.com/download/9682919797e53ce79c185a851587e5a575574dff</link>
</item>
<item>
<title>Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation (Paper)</title>
<description>@article{12:43,author={Ryota Tomioka and Taiji Suzuki and Masashi Sugiyama}, Title={Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/tomioka11a/tomioka11a.pdf}}</description>
<link>https://academictorrents.com/download/7bcab37cc9727bef5eac69a8b43826f99d7493c2</link>
</item>
<item>
<title>Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation (Paper)</title>
<description>@article{7:87,author={Magnus Ekdahl and Timo Koski}, Title={Bounds for the Loss in Probability of Correct Classification Under Model Based Approximation},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/ekdahl06a/ekdahl06a.pdf}}</description>
<link>https://academictorrents.com/download/0fc69cb913852ee93543f0fe741755e03ba081b4</link>
</item>
<item>
<title>Efficient SVM Training Using Low-Rank Kernel Representations (Kernel Machines Section) (Paper)</title>
<description>@article{2:12,author={Shai Fine and Katya Scheinberg}, Title={Efficient SVM Training Using Low-Rank Kernel Representations
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/pekalska01a/pekalska01a.pdf}}</description>
<link>https://academictorrents.com/download/f7e3faa34b142adb79f3c869e33a7cbc2fc3cc19</link>
</item>
<item>
<title>Hyper-Sparse Optimal Aggregation (Paper)</title>
<description>@article{12:50,author={Stphane Gaffas and Guillaume Lecu}, Title={Hyper-Sparse Optimal Aggregation},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/gaiffas11a/gaiffas11a.pdf}}</description>
<link>https://academictorrents.com/download/9fdf62245fad8cc91904e1d6db646b3cd2af6b8e</link>
</item>
<item>
<title>DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model (Paper)</title>
<description>@article{12:33,author={Shohei Shimizu and Takanori Inazumi and Yasuhiro Sogawa and Aapo Hyvrinen and Yoshinobu Kawahara and Takashi Washio and Patrik O. Hoyer and Kenneth Bollen}, Title={DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/shimizu11a/shimizu11a.pdf}}</description>
<link>https://academictorrents.com/download/65bc332a9ff22064b7d377174051e15955826649</link>
</item>
<item>
<title>Anytime Learning of Decision Trees (Paper)</title>
<description>@article{8:33,author={Saher Esmeir and Shaul Markovitch}, Title={Anytime Learning of Decision Trees},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/esmeir07a/esmeir07a.pdf}}</description>
<link>https://academictorrents.com/download/3f8d53cdaca8ec8852963bef9f37d60d7309860e</link>
</item>
<item>
<title>New Algorithms for Efficient High-Dimensional Nonparametric Classification (Paper)</title>
<description>@article{7:41,author={Ting Liu and Andrew W. Moore and Alexander Gray}, Title={New Algorithms for Efficient High-Dimensional Nonparametric Classification},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/liu06a/liu06a.pdf}}</description>
<link>https://academictorrents.com/download/1311aa4cb92a3da7928a5dea4c43de7a6840bb0d</link>
</item>
<item>
<title>Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting (Special Topic on Inductive Programming) (Paper)</title>
<description>@article{7:11,author={Andrea Passerini and Paolo Frasconi and Luc De Raedt}, Title={Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting (Special Topic on Inductive Programming)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/passerini06a/passerini06a.pdf}}</description>
<link>https://academictorrents.com/download/d69b2a90865890327ccb1386080218970d16e5d4</link>
</item>
<item>
<title>PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers (Paper)</title>
<description>@article{8:52,author={Franois Laviolette and Mario Marchand}, Title={PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/laviolette07a/laviolette07a.pdf}}</description>
<link>https://academictorrents.com/download/48770b460f934ebe6ccc2a05d9e913fa62ccd5a8</link>
</item>
<item>
<title>Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results (Paper)</title>
<description>@article{8:28,author={Peter L. Bartlett and Ambuj Tewari}, Title={Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/bartlett07a/bartlett07a.pdf}}</description>
<link>https://academictorrents.com/download/8e34e6991e7bb8c97b82ab309623e5602a8f0610</link>
</item>
<item>
<title>Dimension Reduction in Text Classification with Support Vector Machines (Paper)</title>
<description>@article{6:2,author={Hyunsoo Kim and Peg Howland and Haesun Park}, Title={Dimension Reduction in Text Classification with Support Vector Machines},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/kim05a/kim05a.pdf}}</description>
<link>https://academictorrents.com/download/2b73ffdc6583445eb35d6555d487444f15fb789d</link>
</item>
<item>
<title>Evolutionary Function Approximation for Reinforcement Learning (Paper)</title>
<description>@article{7:31,author={Shimon Whiteson and Peter Stone}, Title={Evolutionary Function Approximation for Reinforcement Learning},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/whiteson06a/whiteson06a.pdf}}</description>
<link>https://academictorrents.com/download/a7bc5f6bc983877b6fdfa38167d15440e321b9f2</link>
</item>
<item>
<title>Active Learning with Feedback on Features and Instances (Paper)</title>
<description>@article{7:61,author={Hema Raghavan and Omid Madani and Rosie Jones}, Title={Active Learning with Feedback on Features and Instances},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/raghavan06a/raghavan06a.pdf}}</description>
<link>https://academictorrents.com/download/0e91d239c92daf6cefe25e8314fa13e48baa1105</link>
</item>
<item>
<title>Theoretical Analysis of Bayesian Matrix Factorization (Paper)</title>
<description>@article{12:79,author={Shinichi Nakajima and Masashi Sugiyama}, Title={Theoretical Analysis of Bayesian Matrix Factorization},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/nakajima11a/nakajima11a.pdf}}</description>
<link>https://academictorrents.com/download/15fe36f7944a8d40c7e3dbdb9c3cb2c94ffabdc2</link>
</item>
<item>
<title>Large Scale Multiple Kernel Learning (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:57,author={Sren Sonnenburg and Gunnar Rtsch and Christin Schfer and Bernhard Schlkopf}, Title={Large Scale Multiple Kernel Learning (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/sonnenburg06a/sonnenburg06a.pdf}}</description>
<link>https://academictorrents.com/download/eb2f99fb247f0f374e63f0efd4a6874466658f31</link>
</item>
<item>
<title>Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation (Paper)</title>
<description>@article{12:1,author={Yizhao Ni and Craig Saunders and Sandor Szedmak and Mahesan Niranjan}, Title={Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/ni11a/ni11a.pdf}}</description>
<link>https://academictorrents.com/download/1376e352dc93e1e2066c34531e9f5f39ad442bca</link>
</item>
<item>
<title>Group Lasso Estimation of High-dimensional Covariance Matrices (Paper)</title>
<description>@article{12:98,author={Jrmie Bigot and Rolando J. Biscay and Jean-Michel Loubes and Lillian Muiz-Alvarez}, Title={Group Lasso Estimation of High-dimensional Covariance Matrices},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/bigot11a/bigot11a.pdf}}</description>
<link>https://academictorrents.com/download/b94b1d2d051795e089f6a3e99ae13f77ec7d2719</link>
</item>
<item>
<title>A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events (Paper)</title>
<description>@article{7:70,author={Shalabh Bhatnagar and Vivek S. Borkar and Madhukar Akarapu}, Title={A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/bhatnagar06a/bhatnagar06a.pdf}}</description>
<link>https://academictorrents.com/download/e40d76248365e1e8d49d052f1b0c2eb3aa3d7a6e</link>
</item>
<item>
<title>Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections (Paper)</title>
<description>@article{8:83,author={Ping Li and Trevor J. Hastie and Kenneth W. Church}, Title={Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/li07b/li07b.pdf}}</description>
<link>https://academictorrents.com/download/b6a11a170af5a00d6a1ccb210305a0d1cec566f2</link>
</item>
<item>
<title>Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters (Special Topic on Model Selection) (Paper)</title>
<description>@article{8:31,author={Gavin C. Cawley and Nicola L. C. Talbot}, Title={Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters (Special Topic on Model Selection)},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/cawley07a/cawley07a.pdf}}</description>
<link>https://academictorrents.com/download/08b92c86b378c859ff1a1cae59db757f3ac28f9f</link>
</item>
<item>
<title>Separating Models of Learning from Correlated and Uncorrelated Data (Special Topic on the Conference on Learning Theory 2005) (Paper)</title>
<description>@article{8:10,author={Ariel Elbaz and Homin K. Lee and Rocco A. Servedio and Andrew Wan}, Title={Separating Models of Learning from Correlated and Uncorrelated Data (Special Topic on the Conference on Learning Theory 2005)},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/elbaz07a/elbaz07a.pdf}}</description>
<link>https://academictorrents.com/download/b591e6306d970a3db3a2d7217a4d2ddf38516707</link>
</item>
<item>
<title>Learning Equivariant Functions with Matrix Valued Kernels (Paper)</title>
<description>@article{8:15,author={Marco Reisert and Hans Burkhardt}, Title={Learning Equivariant Functions with Matrix Valued Kernels},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/reisert07a/reisert07a.pdf}}</description>
<link>https://academictorrents.com/download/46a545c2336e13e237de06acbc584afd7a1cded7</link>
</item>
<item>
<title>Margin Trees for High-dimensional Classification (Paper)</title>
<description>@article{8:23,author={Robert Tibshirani and Trevor Hastie}, Title={Margin Trees for High-dimensional Classification},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/tibshirani07a/tibshirani07a.pdf}}</description>
<link>https://academictorrents.com/download/26038553c5011297c6784264b8895edc840a130a</link>
</item>
<item>
<title>Bayesian Quadratic Discriminant Analysis (Paper)</title>
<description>@article{8:46,author={Santosh Srivastava and Maya R. Gupta and Bla A. Frigyik}, Title={Bayesian Quadratic Discriminant Analysis},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/srivastava07a/srivastava07a.pdf}}</description>
<link>https://academictorrents.com/download/1a41a2ea96101eef06364ea8dc31cfff7ee6abef</link>
</item>
<item>
<title>The Interplay of Optimization and Machine Learning Research (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:46,author={Kristin P. Bennett and Emilio Parrado-Hernndez}, Title={The Interplay of Optimization and Machine Learning Research (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/MLOPT-intro06a/MLOPT-intro06a.pdf}}</description>
<link>https://academictorrents.com/download/f59218767a5dade9760abec4d64dd1ef71bc7581</link>
</item>
<item>
<title>Bayesian Generalized Kernel Mixed Models (Paper)</title>
<description>@article{12:5,author={Zhihua Zhang and Guang Dai and Michael I. Jordan}, Title={Bayesian Generalized Kernel Mixed Models},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/zhang11a/zhang11a.pdf}}</description>
<link>https://academictorrents.com/download/5aa43bec0cb3510349f9314a9cf9f7a57fa9678f</link>
</item>
<item>
<title>Minimum Description Length Penalization for Group and Multi-Task Sparse Learning (Paper)</title>
<description>@article{12:16,author={Paramveer S. Dhillon and Dean Foster and Lyle H. Ungar}, Title={Minimum Description Length Penalization for Group and Multi-Task Sparse Learning},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/dhillon11a/dhillon11a.pdf}}</description>
<link>https://academictorrents.com/download/95477d6818dba2c532684af7bd9a6d645b0c14d5</link>
</item>
<item>
<title>Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling (Paper)</title>
<description>@article{8:44,author={Miroslav Dudk and Steven J. Phillips and Robert E. Schapire}, Title={Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/dudik07a/dudik07a.pdf}}</description>
<link>https://academictorrents.com/download/ee546a96c061eeaaac4aadb9f94cae166ad892b8</link>
</item>
<item>
<title>Universality, Characteristic Kernels and RKHS Embedding of Measures (Paper)</title>
<description>@article{12:70,author={Bharath K. Sriperumbudur and Kenji Fukumizu and Gert R.G. Lanckriet}, Title={Universality, Characteristic Kernels and RKHS Embedding of Measures},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/sriperumbudur11a/sriperumbudur11a.pdf}}</description>
<link>https://academictorrents.com/download/517b5ac8eca29f7553968a5a93f8e500f8f6243b</link>
</item>
<item>
<title>An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression (Paper)</title>
<description>@article{8:54,author={Kwangmoo Koh and Seung-Jean Kim and Stephen Boyd}, Title={An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/koh07a/koh07a.pdf}}</description>
<link>https://academictorrents.com/download/2deb1384378d8b6b777db438b4f57ff7866274a5</link>
</item>
<item>
<title>Refinable Kernels (Paper)</title>
<description>@article{8:71,author={Yuesheng Xu and Haizhang Zhang}, Title={Refinable Kernels},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/xu07a/xu07a.pdf}}</description>
<link>https://academictorrents.com/download/27a1cc2a72cf88a10772e3e7ff8d03c3e4bb4a0d</link>
</item>
<item>
<title>Linear Programs for Hypotheses Selection in Probabilistic Inference Models (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:49,author={Anders Bergkvist and Peter Damaschke and  Marcel Lthi}, Title={Linear Programs for Hypotheses Selection in Probabilistic Inference Models (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/bergkvist06a/bergkvist06a.pdf}}</description>
<link>https://academictorrents.com/download/981132f46a26b773c8c1a9396372ce6546861867</link>
</item>
<item>
<title>Core Vector Machines: Fast SVM Training on Very Large Data Sets (Paper)</title>
<description>@article{6:13,author={Ivor W. Tsang and James T. Kwok and Pak-Ming Cheung}, Title={Core Vector Machines: Fast SVM Training on Very Large Data Sets},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/tsang05a/tsang05a.pdf}}</description>
<link>https://academictorrents.com/download/5f89897c06b7151321830600009ef153f6b45941</link>
</item>
<item>
<title>A Linear Non-Gaussian Acyclic Model for Causal Discovery (Paper)</title>
<description>@article{7:72,author={Shohei Shimizu and Patrik O. Hoyer and Aapo Hyvrinen and Antti Kerminen}, Title={A Linear Non-Gaussian Acyclic Model for Causal Discovery},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/shimizu06a/shimizu06a.pdf}}</description>
<link>https://academictorrents.com/download/83c385e427011f7e57c43b389adb63247ac98490</link>
</item>
<item>
<title>Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples (Paper)</title>
<description>@article{7:85,author={Mikhail Belkin and Partha Niyogi and Vikas Sindhwani}, Title={Manifold  Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/belkin06a/belkin06a.pdf}}</description>
<link>https://academictorrents.com/download/f3a3b1a97bd8fdb762685f05318b822e295fa059</link>
</item>
<item>
<title>Large Scale Transductive SVMs (Paper)</title>
<description>@article{7:62,author={Ronan Collobert and Fabian Sinz and Jason Weston and Lon Bottou}, Title={Large Scale Transductive SVMs},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/collobert06a/collobert06a.pdf}}</description>
<link>https://academictorrents.com/download/fe5196636bb4f1f391220fbe64d73393c8ef1481</link>
</item>
<item>
<title>Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis (Paper)</title>
<description>@article{7:16,author={Tonatiuh Pea Centeno and Neil D. Lawrence}, Title={Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/centeno06a/centeno06a.pdf}}</description>
<link>https://academictorrents.com/download/34e81945b75f04fc9d761ff03107482eb6fdbda6</link>
</item>
<item>
<title>Cumulative Distribution Networks and the Derivative-sum-product Algorithm: Models and Inference for Cumulative Distribution Functions on Graphs (Paper)</title>
<description>@article{12:10,author={Jim C. Huang and Brendan J. Frey}, Title={Cumulative Distribution Networks and the Derivative-sum-product Algorithm: Models and Inference for Cumulative Distribution Functions on Graphs},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/huang11a/huang11a.pdf}}</description>
<link>https://academictorrents.com/download/a14356df526b633b5280cecf9f65e4b6141b609c</link>
</item>
<item>
<title>Structured Variable Selection with Sparsity-Inducing Norms (Paper)</title>
<description>@article{12:84,author={Rodolphe Jenatton and Jean-Yves Audibert and Francis Bach}, Title={Structured Variable Selection with Sparsity-Inducing Norms},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/jenatton11b/jenatton11b.pdf}}</description>
<link>https://academictorrents.com/download/a9da48e21b8fb1dbf8f7736d5f04a4e688c9b3f7</link>
</item>
<item>
<title>Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining (Paper)</title>
<description>@article{10:14,author={Petra Kralj Novak and Nada Lavra and Geoffrey I. Webb}, Title={Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/kralj-novak09a/kralj-novak09a.pdf}}</description>
<link>https://academictorrents.com/download/2cf0863d0ecaeffdf82e75ae8b572cc0da2ee4f6</link>
</item>
<item>
<title>Learning a Hidden Hypergraph (Paper)</title>
<description>@article{7:79,author={Dana Angluin and Jiang Chen}, Title={Learning a Hidden Hypergraph},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/angluin06a/angluin06a.pdf}}</description>
<link>https://academictorrents.com/download/0072e334eb0341d1a3aa1228d16142da5da7729a</link>
</item>
<item>
<title>Learning with Structured Sparsity (Paper)</title>
<description>@article{12:103,author={Junzhou Huang and Tong Zhang and Dimitris Metaxas}, Title={Learning with Structured Sparsity},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/huang11b/huang11b.pdf}}</description>
<link>https://academictorrents.com/download/47a3b370d978d2db44d8d4b16198910f8008eece</link>
</item>
<item>
<title>A Family of Simple Non-Parametric Kernel Learning Algorithms (Paper)</title>
<description>@article{12:36,author={Jinfeng Zhuang and Ivor W. Tsang and Steven C.H. Hoi}, Title={A Family of Simple Non-Parametric Kernel Learning Algorithms},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/zhuang11a/zhuang11a.pdf}}</description>
<link>https://academictorrents.com/download/25e2c246cb979b5177f6f503e7b9936e6502e9e2</link>
</item>
<item>
<title>On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines (Kernel Machines Section) (Paper)</title>
<description>@article{2:13,author={Koby Crammer and Yoram Singer}, Title={On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/pekalska01a/rev1/pekalska01ar1.pdf}}</description>
<link>https://academictorrents.com/download/e5e4db66ae2b6757f9a4ddca5ea6793db4c9b267</link>
</item>
<item>
<title>X-Armed Bandits (Paper)</title>
<description>@article{12:46,author={Sbastien Bubeck and Rmi Munos and Gilles Stoltz and Csaba Szepesvri}, Title={X-Armed Bandits},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/bubeck11a/bubeck11a.pdf}}</description>
<link>https://academictorrents.com/download/82a415bed4fd188b34ea7b50c2b92c5659428a50</link>
</item>
<item>
<title>Dynamics and Generalization Ability of LVQ Algorithms (Paper)</title>
<description>@article{8:13,author={Michael Biehl and Anarta Ghosh and Barbara Hammer}, Title={Dynamics and Generalization Ability of LVQ Algorithms},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/biehl07a/biehl07a.pdf}}</description>
<link>https://academictorrents.com/download/d70bf26ff67cac69225ba1aad75beb599dcb47bb</link>
</item>
<item>
<title>Dirichlet Process Mixtures of Generalized Linear Models (Paper)</title>
<description>@article{12:54,author={Lauren A. Hannah and David M. Blei and Warren B. Powell}, Title={Dirichlet Process Mixtures of Generalized Linear Models},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/hannah11a/hannah11a.pdf}}</description>
<link>https://academictorrents.com/download/3661cf7a069b9defb6cf5dd385e4ba5c03c43052</link>
</item>
<item>
<title>Sparse Linear Identifiable Multivariate Modeling (Paper)</title>
<description>@article{12:24,author={Ricardo Henao and Ole Winther}, Title={Sparse Linear Identifiable Multivariate Modeling},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/henao11a/henao11a.pdf}}</description>
<link>https://academictorrents.com/download/d2a6b778455b4284bfe0a44ea794ce35cf4573f4</link>
</item>
<item>
<title>Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization (Paper)</title>
<description>@article{14:17,author={Shai Shalev-Shwartz and Tong Zhang}, Title={Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/shalev-shwartz13a/shalev-shwartz13a.pdf}}</description>
<link>https://academictorrents.com/download/ba016e245fb44ec204437338eecd1cf4b94c65a8</link>
</item>
<item>
<title>Double Updating Online Learning (Paper)</title>
<description>@article{12:44,author={Peilin Zhao and Steven C.H. Hoi and Rong Jin}, Title={Double Updating Online Learning},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/zhao11a/zhao11a.pdf}}</description>
<link>https://academictorrents.com/download/0bc529b79f14c92c8a4b273b3197601aafb4c5d0</link>
</item>
<item>
<title>Learning Nondeterministic Classifiers (Paper)</title>
<description>@article{10:79,author={Juan Jos del Coz and Jorge Dez and Antonio Bahamonde}, Title={Learning Nondeterministic Classifiers},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/delcoz09a/delcoz09a.pdf}}</description>
<link>https://academictorrents.com/download/5d1dbaf04b0e5b1b254f4167267f3b345a8e6d7e</link>
</item>
<item>
<title>Stagewise Lasso (Paper)</title>
<description>@article{8:89,author={Peng Zhao and Bin Yu}, Title={Stagewise Lasso},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/zhao07a/zhao07a.pdf}}</description>
<link>https://academictorrents.com/download/defc2d193d959e9819bddb6d9ca11eaa70802569</link>
</item>
<item>
<title>Scikit-learn: Machine Learning in Python (Paper)</title>
<description>@article{12:85,author={Fabian Pedregosa and Gal Varoquaux and Alexandre Gramfort and Vincent Michel and Bertrand Thirion and Olivier Grisel and Mathieu Blondel and Peter Prettenhofer and Ron Weiss and Vincent Dubourg and Jake Vanderplas and Alexandre Passos and David Cournapeau and Matthieu Brucher and Matthieu Perrot and douard Duchesnay}, Title={Scikit-learn: Machine Learning in Python},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf}}</description>
<link>https://academictorrents.com/download/5ba4939a00a9b21629a0ad7d376898b768d997a3</link>
</item>
<item>
<title>Parallel Algorithm for Learning Optimal Bayesian Network Structure (Paper)</title>
<description>@article{12:73,author={Yoshinori Tamada and Seiya Imoto and Satoru Miyano}, Title={Parallel Algorithm for Learning Optimal Bayesian Network Structure},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/tamada11a/tamada11a.pdf}}</description>
<link>https://academictorrents.com/download/cc032abd599138c3215423c5a746c4f24b70714c</link>
</item>
<item>
<title>Learning Multi-modal Similarity (Paper)</title>
<description>@article{12:15,author={Brian McFee and Gert Lanckriet}, Title={Learning Multi-modal Similarity},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/mcfee11a/mcfee11a.pdf}}</description>
<link>https://academictorrents.com/download/34ca78c54a4f49615dd060a9d31fffaf4646ba89</link>
</item>
<item>
<title>Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood (Paper)</title>
<description>@article{12:63,author={Alexandra M. Carvalho and Teemu Roos and Arlindo L. Oliveira and Petri Myllymki}, Title={Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/carvalho11a/carvalho11a.pdf}}</description>
<link>https://academictorrents.com/download/43174c736752d532c6beef2db3a51f160f3e20d4</link>
</item>
<item>
<title>CARP: Software for Fishing Out Good Clustering Algorithms (Paper)</title>
<description>@article{12:3,author={Volodymyr Melnykov and Ranjan Maitra}, Title={CARP: Software for Fishing Out Good Clustering Algorithms},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/melnykov11a/melnykov11a.pdf}}</description>
<link>https://academictorrents.com/download/5e484b1c2b27bcb2666f500789826af07d052fe5</link>
</item>
<item>
<title>Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression (Paper)</title>
<description>@article{8:30,author={Shantanu Chakrabartty and Gert Cauwenberghs}, Title={Gini Support Vector Machine: Quadratic Entropy Based Robust Multi-Class Probability Regression},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/chakrabartty07a/chakrabartty07a.pdf}}</description>
<link>https://academictorrents.com/download/edf90fe1660c45f75c07a34842d2c6b2361e13e7</link>
</item>
<item>
<title>Learning Minimum Volume Sets (Paper)</title>
<description>@article{7:24,author={Clayton D. Scott and Robert D. Nowak}, Title={Learning Minimum Volume Sets},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/scott06a/scott06a.pdf}}</description>
<link>https://academictorrents.com/download/c672b8774ca121ea3393cdbc25be2d99d17cf79f</link>
</item>
<item>
<title>Value Regularization and Fenchel Duality (Paper)</title>
<description>@article{8:17,author={Ryan M. Rifkin and Ross A. Lippert}, Title={Value Regularization and Fenchel Duality},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/rifkin07a/rifkin07a.pdf}}</description>
<link>https://academictorrents.com/download/465d5a852f238ee8a686c9cbaab84f447477c45d</link>
</item>
<item>
<title>On the Learnability of Shuffle Ideals (Paper)</title>
<description>@article{14:48,author={Dana Angluin and James Aspnes and Sarah Eisenstat and Aryeh Kontorovich}, Title={On the Learnability of Shuffle Ideals},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/angluin13a/angluin13a.pdf}}</description>
<link>https://academictorrents.com/download/1994966b15a813e041fd3b12333cbb91fd51ee86</link>
</item>
<item>
<title>Nonlinear Models Using Dirichlet Process Mixtures (Paper)</title>
<description>@article{10:63,author={Babak Shahbaba and Radford Neal}, Title={Nonlinear Models Using Dirichlet Process Mixtures},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/shahbaba09a/shahbaba09a.pdf}}</description>
<link>https://academictorrents.com/download/077943f332927f73167acaf86c83073fcdbb0f69</link>
</item>
<item>
<title>Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning (Paper)</title>
<description>@article{12:39,author={Dorota Gowacka and John Shawe-Taylor and Alex Clark and Colin de la Higuera and Mark Johnson}, Title={Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/glowacka11a/glowacka11a.pdf}}</description>
<link>https://academictorrents.com/download/71e1c9692c556e252aa7e2f4715c419ee447039b</link>
</item>
<item>
<title>Learning Horn Expressions with LOGAN-H (Paper)</title>
<description>@article{8:20,author={Marta Arias and Roni Khardon and Jrme Maloberti}, Title={Learning Horn Expressions with LOGAN-H},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/arias07a/arias07a.pdf}}</description>
<link>https://academictorrents.com/download/f4e3b96b5b9e4e4974f218341b30f4d7f59afa8c</link>
</item>
<item>
<title>Posterior Sparsity in Unsupervised Dependency Parsing (Paper)</title>
<description>@article{12:14,author={Jennifer Gillenwater and Kuzman Ganchev and Joo Graa and Fernando Pereira and Ben Taskar}, Title={Posterior Sparsity in Unsupervised Dependency Parsing},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/gillenwater11a/gillenwater11a.pdf}}</description>
<link>https://academictorrents.com/download/f933c7d421ba53868b3a03bd73728294abdb70d5</link>
</item>
<item>
<title>lp-Norm Multiple Kernel Learning (Paper)</title>
<description>@article{12:26,author={Marius Kloft and Ulf Brefeld and Sren Sonnenburg and Alexander Zien}, Title={lp-Norm Multiple Kernel Learning},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/kloft11a/kloft11a.pdf}}</description>
<link>https://academictorrents.com/download/29c6ca8dd86b10b4d0870f86dac6e78eebe268a1</link>
</item>
<item>
<title>Step Size Adaptation in Reproducing Kernel Hilbert Space (Paper)</title>
<description>@article{7:40,author={S. V. N. Vishwanathan and Nicol N. Schraudolph and Alex J. Smola}, Title={Step Size Adaptation in Reproducing Kernel Hilbert Space},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/schraudolph06a/schraudolph06a.pdf}}</description>
<link>https://academictorrents.com/download/5640eae7ca2e270385c383c9c2e1b7969d95c246</link>
</item>
<item>
<title>Anechoic Blind Source Separation Using Wigner Marginals (Paper)</title>
<description>@article{12:30,author={Lars Omlor and Martin A. Giese}, Title={Anechoic Blind Source Separation Using Wigner Marginals},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/omlor11a/omlor11a.pdf}}</description>
<link>https://academictorrents.com/download/3f009f0f96c7fc816eaff965df80a2d33d8237c9</link>
</item>
<item>
<title>Distance Metric Learning for Large Margin Nearest Neighbor Classification (Paper)</title>
<description>@article{10:9,author={Kilian Q. Weinberger and Lawrence K. Saul}, Title={Distance Metric Learning for Large Margin Nearest Neighbor Classification},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/weinberger09a/weinberger09a.pdf}}</description>
<link>https://academictorrents.com/download/530e4635cf63a20a58a2e032707730ca226a61f0</link>
</item>
<item>
<title>Efficient Program Synthesis Using Constraint Satisfaction in Inductive Logic Programming (Paper)</title>
<description>@article{14:116,author={John Ahlgren and Shiu Yin Yuen}, Title={Efficient Program Synthesis Using Constraint Satisfaction in Inductive Logic Programming},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/ahlgren13a/ahlgren13a.pdf}}</description>
<link>https://academictorrents.com/download/d65a9e6281772cd06869014308ca69018f45e2f5</link>
</item>
<item>
<title>Discriminative Learning Under Covariate Shift (Paper)</title>
<description>@article{10:75,author={Steffen Bickel and Michael Brckner and Tobias Scheffer}, Title={Discriminative Learning Under Covariate Shift},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/bickel09a/bickel09a.pdf}}</description>
<link>https://academictorrents.com/download/88f059942483326a34a7990450d440bba733ae4e</link>
</item>
<item>
<title>A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes (Paper)</title>
<description>@article{12:48,author={Stphane Ross and Joelle Pineau and Brahim Chaib-draa and Pierre Kreitmann}, Title={A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/ross11a/ross11a.pdf}}</description>
<link>https://academictorrents.com/download/55f4ffc91509ab0f716cb86c642585a25bfb93cd</link>
</item>
<item>
<title>In All Likelihood, Deep Belief Is Not Enough (Paper)</title>
<description>@article{12:94,author={Lucas Theis and Sebastian Gerwinn and Fabian Sinz and Matthias Bethge}, Title={In All Likelihood, Deep Belief Is Not Enough},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/theis11a/theis11a.pdf}}</description>
<link>https://academictorrents.com/download/9f5e775d7ce6272037c3bd72df4390c493acfa7b</link>
</item>
<item>
<title>Infinite- Limits For Tikhonov Regularization (Paper)</title>
<description>@article{7:30,author={Ross A. Lippert and Ryan M. Rifkin}, Title={Infinite- Limits For Tikhonov Regularization},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/lippert06a/lippert06a.pdf}}</description>
<link>https://academictorrents.com/download/d9af67a76d835084cfec13a08989536613c5b34e</link>
</item>
<item>
<title>Learning a Mahalanobis Metric from Equivalence Constraints (Paper)</title>
<description>@article{6:32,author={Aharon Bar-Hillel and Tomer Hertz and Noam Shental and Daphna Weinshall}, Title={Learning a Mahalanobis Metric from Equivalence Constraints},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/bar-hillel05a/bar-hillel05a.pdf}}</description>
<link>https://academictorrents.com/download/0a1cf731c65a7252487bf400a035eee85da7319a</link>
</item>
<item>
<title>Text Chunking based on a Generalization of Winnow (Paper)</title>
<description>@article{2:27,author={Tong Zhang and Fred Damerau and David Johnson}, Title={Text Chunking based on a Generalization of Winnow},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/zhang02b/zhang02b.pdf}}</description>
<link>https://academictorrents.com/download/1fe06c45173b62ff473cc3da044ec688e8c862a0</link>
</item>
<item>
<title>Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data (Paper)</title>
<description>@article{8:25,author={Charles Sutton and Andrew McCallum and Khashayar Rohanimanesh}, Title={Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/sutton07a/sutton07a.pdf}}</description>
<link>https://academictorrents.com/download/2cc1158f0537514d2050c24d07a09ac9bebfd3da</link>
</item>
<item>
<title>JKernelMachines: A Simple Framework for Kernel Machines (Paper)</title>
<description>@article{14:44,author={David Picard and Nicolas Thome and Matthieu Cord}, Title={JKernelMachines: A Simple Framework for Kernel Machines},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/picard13a/picard13a.pdf}}</description>
<link>https://academictorrents.com/download/525232019cb30357948aee5a5d0132c5788a739c</link>
</item>
<item>
<title>NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM (Paper)</title>
<description>@article{10:21,author={Pradip Ghanty and Samrat Paul and Nikhil R. Pal}, Title={NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/ghanty09a/ghanty09a.pdf}}</description>
<link>https://academictorrents.com/download/a0c95108809d820da29be0b47242fcd7823d952e</link>
</item>
<item>
<title>Support Vector Machine Active Learning with Applications to Text Classification (Paper)</title>
<description>@article{2:3,author={Simon Tong and Daphne Koller}, Title={Support Vector Machine Active Learning with Applications to Text Classification},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/tong01a/tong01a.pdf}}</description>
<link>https://academictorrents.com/download/639b6fa478c23316be69c49ea4e42dfc3d73371d</link>
</item>
<item>
<title>On Using Extended Statistical Queries to Avoid Membership Queries (Paper)</title>
<description>@article{2:18,author={Nader H. Bshouty and Vitaly Feldman}, Title={On Using Extended Statistical Queries to Avoid Membership Queries},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/genton01a/genton01a.pdf}}</description>
<link>https://academictorrents.com/download/b0ba3a0e719c2b3ee91c38662dc0909c6786ab9b</link>
</item>
<item>
<title>Robust Approximate Bilinear Programming for Value Function Approximation (Paper)</title>
<description>@article{12:92,author={Marek Petrik and Shlomo Zilberstein}, Title={Robust Approximate Bilinear Programming for Value Function Approximation},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/petrik11a/petrik11a.pdf}}</description>
<link>https://academictorrents.com/download/453d9ce6d3a6f23b5909fbefbbab84b0c74bb855</link>
</item>
<item>
<title>A Generalized Kernel Approach to Dissimilarity-based Classification (Kernel Machines Section) (Paper)</title>
<description>@article{2:10,author={Elzbieta Pekalska and Pavel Paclik and Robert P.W. Duin}, Title={A Generalized Kernel Approach to Dissimilarity-based Classification
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/tax01a/tax01a.pdf}}</description>
<link>https://academictorrents.com/download/620871753fb3a80f0177dcc20a193648dbb8197d</link>
</item>
<item>
<title>Worst-Case Analysis of Selective Sampling for Linear Classification (Paper)</title>
<description>@article{7:44,author={Nicol Cesa-Bianchi and Claudio Gentile and Luca Zaniboni}, Title={Worst-Case Analysis of Selective Sampling for Linear Classification},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/cesa-bianchi06b/cesa-bianchi06b.pdf}}</description>
<link>https://academictorrents.com/download/6337ffb8648d6c0dd01b9d5463f86c66f7b1c96f</link>
</item>
<item>
<title>Unsupervised Supervised Learning II: Margin-Based Classification Without Labels (Paper)</title>
<description>@article{12:96,author={Krishnakumar Balasubramanian and Pinar Donmez and Guy Lebanon}, Title={Unsupervised Supervised Learning II: Margin-Based Classification Without Labels},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/balasubramanian11a/balasubramanian11a.pdf}}</description>
<link>https://academictorrents.com/download/abfba3928f07f06af6d2f37f98c56731144689dd</link>
</item>
<item>
<title>A Classification Framework for Anomaly Detection (Paper)</title>
<description>@article{6:8,author={Ingo Steinwart and Don Hush and Clint Scovel}, Title={A Classification Framework for Anomaly Detection},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/steinwart05a/steinwart05a.pdf}}</description>
<link>https://academictorrents.com/download/95df4fceb716fe136e921db7821686a6f1665020</link>
</item>
<item>
<title>Learning to Classify Ordinal Data: The Data Replication Method (Paper)</title>
<description>@article{8:50,author={Jaime S. Cardoso and Joaquim F. Pinto da Costa}, Title={Learning to Classify Ordinal Data: The Data Replication Method},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/cardoso07a/cardoso07a.pdf}}</description>
<link>https://academictorrents.com/download/8381325b62839851a03b66edfa8fc395a6c73abd</link>
</item>
<item>
<title>MULAN: A Java Library for Multi-Label Learning (Paper)</title>
<description>@article{12:71,author={Grigorios Tsoumakas and Eleftherios Spyromitros-Xioufis and Jozef Vilcek and Ioannis Vlahavas}, Title={MULAN: A Java Library for Multi-Label Learning},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/tsoumakas11a/tsoumakas11a.pdf}}</description>
<link>https://academictorrents.com/download/06e59f0717ac2e28a288ec03601d0de30f8bb28f</link>
</item>
<item>
<title>Information Rates of Nonparametric Gaussian Process Methods (Paper)</title>
<description>@article{12:60,author={Aad van der Vaart and Harry van Zanten}, Title={Information Rates of Nonparametric Gaussian Process Methods},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/vandervaart11a/vandervaart11a.pdf}}</description>
<link>https://academictorrents.com/download/73fdec12ac6011e192ec55f460dfba81a166d40e</link>
</item>
<item>
<title>Stochastic Methods for l1-regularized Loss Minimization (Paper)</title>
<description>@article{12:52,author={Shai Shalev-Shwartz and Ambuj Tewari}, Title={Stochastic Methods for l1-regularized Loss Minimization},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/shalev-shwartz11a/shalev-shwartz11a.pdf}}</description>
<link>https://academictorrents.com/download/4643cebc79b7e7f77ca33093c614cb171a4dac9c</link>
</item>
<item>
<title>Experiment Selection for Causal Discovery (Paper)</title>
<description>@article{14:94,author={Antti Hyttinen and Frederick Eberhardt and Patrik O. Hoyer}, Title={Experiment Selection for Causal Discovery},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/hyttinen13a/hyttinen13a.pdf}}</description>
<link>https://academictorrents.com/download/695290318fd1fc5633f883f832bc089bdf840538</link>
</item>
<item>
<title>Linear Programming Relaxations and Belief Propagation -- An Empirical Study (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:68,author={Chen Yanover and Talya Meltzer and Yair Weiss}, Title={Linear Programming Relaxations and Belief Propagation -- An Empirical Study (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/yanover06a/yanover06a.pdf}}</description>
<link>https://academictorrents.com/download/adc57fd2617de4ac0da9741eb465bab36f80d757</link>
</item>
<item>
<title>On Model Selection Consistency of Lasso (Paper)</title>
<description>@article{7:90,author={Peng Zhao and Bin Yu}, Title={On Model Selection Consistency of Lasso},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/zhao06a/zhao06a.pdf}}</description>
<link>https://academictorrents.com/download/9ca348a1e9b6a56f90e53ab1cedf7ab783f55eb6</link>
</item>
<item>
<title>Spam Filtering Using Statistical Data Compression Models (Special Topic on Machine Learning for Computer Security) (Paper)</title>
<description>@article{7:97,author={Andrej Bratko and Gordon V. Cormack and Bogdan Filipi and Thomas R. Lynam and Bla Zupan}, Title={Spam Filtering Using Statistical Data Compression Models
(Special Topic on Machine Learning for Computer Security)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/bratko06a/bratko06a.pdf}}</description>
<link>https://academictorrents.com/download/cfb1b3100fcf1175e88124ae04b49533cdff2c87</link>
</item>
<item>
<title>Differentially Private Empirical Risk Minimization (Paper)</title>
<description>@article{12:29,author={Kamalika Chaudhuri and Claire Monteleoni and Anand D. Sarwate}, Title={Differentially Private Empirical Risk Minimization},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/chaudhuri11a/chaudhuri11a.pdf}}</description>
<link>https://academictorrents.com/download/e81f29f96c44213cb50e33cf457d621a4a298a26</link>
</item>
<item>
<title>Distance Dependent Chinese Restaurant Processes (Paper)</title>
<description>@article{12:74,author={David M. Blei and Peter I. Frazier}, Title={Distance Dependent Chinese Restaurant Processes},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/blei11a/blei11a.pdf}}</description>
<link>https://academictorrents.com/download/e8ab6453351bea02ae0ba4ff3f6749bd4734a203</link>
</item>
<item>
<title>Exploring Strategies for Training Deep Neural Networks (Paper)</title>
<description>@article{10:1,author={Hugo Larochelle and Yoshua Bengio and Jrme Louradour and Pascal Lamblin}, Title={Exploring Strategies for Training Deep Neural Networks},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/larochelle09a/larochelle09a.pdf}}</description>
<link>https://academictorrents.com/download/d712b334f78b0dc31820f9faaf9f3c59c70799b9</link>
</item>
<item>
<title> Ensemble Pruning Via Semi-definite Programming (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:48,author={Yi Zhang and Samuel Burer and W. Nick Street}, Title={ Ensemble Pruning Via Semi-definite Programming (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/zhang06a/zhang06a.pdf}}</description>
<link>https://academictorrents.com/download/dd46e5dd71f1d030fecbeaefe8f160db8a667c4c</link>
</item>
<item>
<title>Analysis of Variance of Cross-Validation Estimators of the Generalization Error (Paper)</title>
<description>@article{6:39,author={Marianthi Markatou and Hong Tian and Shameek Biswas and George Hripcsak}, Title={Analysis of Variance of Cross-Validation Estimators of the Generalization Error},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/markatou05a/markatou05a.pdf}}</description>
<link>https://academictorrents.com/download/822c908e2655cccc42ffe1cdbdc3b79454294032</link>
</item>
<item>
<title>Parameter Screening and Optimisation for ILP using Designed Experiments (Paper)</title>
<description>@article{12:19,author={Ashwin Srinivasan and Ganesh Ramakrishnan}, Title={Parameter Screening and Optimisation for ILP using Designed Experiments},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/srinivasan11a/srinivasan11a.pdf}}</description>
<link>https://academictorrents.com/download/7ee4af34fd1bdd06a40e4a8d7d946f399c919444</link>
</item>
<item>
<title>Exploiting Best-Match Equations for Efficient Reinforcement Learning (Paper)</title>
<description>@article{12:59,author={Harm van Seijen and Shimon Whiteson and Hado van Hasselt and Marco Wiering}, Title={Exploiting Best-Match Equations for Efficient Reinforcement Learning},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/vanseijen11a/vanseijen11a.pdf}}</description>
<link>https://academictorrents.com/download/70c84121a79df3e988df8f214c107352a7a04808</link>
</item>
<item>
<title>Learning Latent Tree Graphical Models (Paper)</title>
<description>@article{12:49,author={Myung Jin Choi and Vincent Y. F. Tan and Animashree Anandkumar and Alan S. Willsky}, Title={Learning Latent Tree Graphical Models},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/choi11b/choi11b.pdf}}</description>
<link>https://academictorrents.com/download/230f679124442f5ce05668b6ebf8a8f9f27337df</link>
</item>
<item>
<title>Kernel Regression in the Presence of Correlated Errors (Paper)</title>
<description>@article{12:55,author={Kris De Brabanter and Jos De Brabanter and Johan A.K. Suykens and Bart De Moor}, Title={Kernel Regression in the Presence of Correlated Errors},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/debrabanter11a/debrabanter11a.pdf}}</description>
<link>https://academictorrents.com/download/5a2e5d42009b860ea5c3967332603f4f5aca49ea</link>
</item>
<item>
<title>Learning with Decision Lists of Data-Dependent Features (Paper)</title>
<description>@article{6:15,author={Mario Marchand and Marina Sokolova}, Title={Learning with Decision Lists of Data-Dependent Features},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/marchand05a/marchand05a.pdf}}</description>
<link>https://academictorrents.com/download/bad76c32d58770622c34f7b34cfb61aaa72b7626</link>
</item>
<item>
<title>Incremental Support Vector Learning: Analysis, Implementation and Applications (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:69,author={Pavel Laskov and Christian Gehl and Stefan Krger and Klaus-Robert Mller}, Title={Incremental Support Vector Learning: Analysis, Implementation and Applications (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/laskov06a/laskov06a.pdf}}</description>
<link>https://academictorrents.com/download/c10a3083a19620e20dd28eec55d39763695469c2</link>
</item>
<item>
<title>Improved Moves for Truncated Convex Models (Paper)</title>
<description>@article{12:2,author={M. Pawan Kumar and Olga Veksler and Philip H.S. Torr}, Title={Improved Moves for Truncated Convex Models},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/kumar11a/kumar11a.pdf}}</description>
<link>https://academictorrents.com/download/9d1759049019d25a6c9cb965116e102a9692698b</link>
</item>
<item>
<title>Marginal Likelihood Integrals for Mixtures of Independence Models (Paper)</title>
<description>@article{10:55,author={Shaowei Lin and Bernd Sturmfels and Zhiqiang Xu}, Title={Marginal Likelihood Integrals for Mixtures of Independence Models},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/lin09a/lin09a.pdf}}</description>
<link>https://academictorrents.com/download/60f075fed1a4fd3d039561039957fa80bdead00c</link>
</item>
<item>
<title>Online Learning with Samples Drawn from Non-identical Distributions (Paper)</title>
<description>@article{10:98,author={Ting Hu and Ding-Xuan Zhou}, Title={Online Learning with Samples Drawn from Non-identical Distributions},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/hu09a/hu09a.pdf}}</description>
<link>https://academictorrents.com/download/bf4dff3c524cb89cad4cefa7d237abbf0278493e</link>
</item>
<item>
<title>Natural Language Processing (Almost) from Scratch (Paper)</title>
<description>@article{12:76,author={Ronan Collobert and Jason Weston and Lon Bottou and Michael Karlen and Koray Kavukcuoglu and Pavel Kuksa}, Title={Natural Language Processing (Almost) from Scratch},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/collobert11a/collobert11a.pdf}}</description>
<link>https://academictorrents.com/download/824fd119b03225610249c0ce6ceae778dcb7e28d</link>
</item>
<item>
<title>Information, Divergence and Risk for Binary Experiments (Paper)</title>
<description>@article{12:22,author={Mark D. Reid and Robert C. Williamson}, Title={Information, Divergence and Risk for Binary Experiments},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/reid11a/reid11a.pdf}}</description>
<link>https://academictorrents.com/download/d905fed7d452becae1f8d4500845f53475ceb50e</link>
</item>
<item>
<title>Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions (Paper)</title>
<description>@article{10:23,author={Sbastien Bubeck and Ulrike von Luxburg}, Title={Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/bubeck09a/bubeck09a.pdf}}</description>
<link>https://academictorrents.com/download/500f721722fc56105b977d78a9dbda71e6b95480</link>
</item>
<item>
<title>A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization (Paper)</title>
<description>@article{10:29,author={Jacob Abernethy and Francis Bach and Theodoros Evgeniou and Jean-Philippe Vert}, Title={A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/abernethy09a/abernethy09a.pdf}}</description>
<link>https://academictorrents.com/download/30cff6756b819112cf94ba44f6f99d527f5a6bb7</link>
</item>
<item>
<title>Inner Product Spaces for Bayesian Networks (Paper)</title>
<description>@article{6:47,author={Atsuyoshi Nakamura and Michael Schmitt and Niels Schmitt and Hans Ulrich Simon}, Title={Inner Product Spaces for Bayesian Networks},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/nakamura05a/nakamura05a.pdf}}</description>
<link>https://academictorrents.com/download/b328509aa436f2928fd95411b75892fa76c154b7</link>
</item>
<item>
<title>Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes (Paper)</title>
<description>@article{12:83,author={Elias Zavitsanos and Georgios Paliouras and George A. Vouros}, Title={Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/zavitsanos11a/zavitsanos11a.pdf}}</description>
<link>https://academictorrents.com/download/f0d7c8957eb8166c781afcb9d4cb800f88dc8d1c</link>
</item>
<item>
<title>Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies (Paper)</title>
<description>@article{7:76,author={Fu Chang and Chin-Chin Lin and Chi-Jen Lu}, Title={Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/chang06a/chang06a.pdf}}</description>
<link>https://academictorrents.com/download/044f66e5f830e8af633c12126e17b83e7ca11159</link>
</item>
<item>
<title>Efficient Computation of Gapped Substring Kernels on Large Alphabets (Paper)</title>
<description>@article{6:45,author={Juho Rousu and John Shawe-Taylor}, Title={Efficient Computation of Gapped Substring Kernels on Large Alphabets},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/rousu05a/rousu05a.pdf}}</description>
<link>https://academictorrents.com/download/dff9129acc8eb68901209bc0e4403440780f2f52</link>
</item>
<item>
<title>Uniform Object Generation for Optimizing One-class Classifiers (Kernel Machines Section) (Paper)</title>
<description>@article{2:9,author={David M.J. Tax and Robert P.W. Duin}, Title={Uniform Object Generation for Optimizing One-class Classifiers
(Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/manevitz01a/manevitz01a.pdf}}</description>
<link>https://academictorrents.com/download/a789d64967f290658051009c71ac12ce521f53f8</link>
</item>
<item>
<title>Approximate Marginals in Latent Gaussian Models (Paper)</title>
<description>@article{12:13,author={Botond Cseke and Tom Heskes}, Title={Approximate Marginals in Latent Gaussian Models},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/cseke11a/cseke11a.pdf}}</description>
<link>https://academictorrents.com/download/46b85abadd8047e372ad9d828267c3caf2010015</link>
</item>
<item>
<title>Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection (Paper)</title>
<description>@article{6:34,author={Koji Tsuda and Gunnar Rtsch and Manfred K. Warmuth}, Title={Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/tsuda05a/tsuda05a.pdf}}</description>
<link>https://academictorrents.com/download/45c959bd128b0d7c8f8bb56a142feace3ded16aa</link>
</item>
<item>
<title>Variational Inference in Nonconjugate Models (Paper)</title>
<description>@article{14:32,author={Chong Wang and David M. Blei}, Title={Variational Inference in Nonconjugate Models},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/wang13b/wang13b.pdf}}</description>
<link>https://academictorrents.com/download/de0d3b62fd9c1efd79ddf106dfe1036d602fa012</link>
</item>
<item>
<title>Efficient Learning of Label Ranking by Soft Projections onto Polyhedra (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:58,author={Shai Shalev-Shwartz and Yoram Singer}, Title={Efficient Learning of Label Ranking by Soft Projections onto Polyhedra (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/shalev-shwartz06a/shalev-shwartz06a.pdf}}</description>
<link>https://academictorrents.com/download/edbb2295a1ce9b8d0a55255c4ea01965b0be4e99</link>
</item>
<item>
<title>A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs (Paper)</title>
<description>@article{6:12,author={S. Sathiya Keerthi and Dennis DeCoste}, Title={A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/keerthi05a/keerthi05a.pdf}}</description>
<link>https://academictorrents.com/download/965bb1f04693a15ad715ed355c6d910bbb81a4dc</link>
</item>
<item>
<title>On the Representer Theorem and Equivalent Degrees of Freedom of SVR (Paper)</title>
<description>@article{8:82,author={Francesco Dinuzzo and Marta Neve and Giuseppe De Nicolao and Ugo Pietro Gianazza}, Title={On the Representer Theorem and Equivalent Degrees of Freedom of SVR},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/dinuzzo07a/dinuzzo07a.pdf}}</description>
<link>https://academictorrents.com/download/a69d1eef1b59b1fe305e6951d80a6ef27608ef25</link>
</item>
<item>
<title>Multiple Kernel Learning Algorithms (Paper)</title>
<description>@article{12:64,author={Mehmet Gnen and Ethem Alpaydn}, Title={Multiple Kernel Learning Algorithms},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/gonen11a/gonen11a.pdf}}</description>
<link>https://academictorrents.com/download/83f0dc25b1c9175df96fbe168bd153226b4fa473</link>
</item>
<item>
<title>Multi-Task Learning for Classification with Dirichlet Process Priors (Paper)</title>
<description>@article{8:2,author={Ya Xue and Xuejun Liao and Lawrence Carin and Balaji Krishnapuram}, Title={Multi-Task Learning for Classification with Dirichlet Process Priors},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/xue07a/xue07a.pdf}}</description>
<link>https://academictorrents.com/download/38cf8e6441722f7ca42d2c2c28fe8248e2f55472</link>
</item>
<item>
<title>Efficient Learning with Partially Observed Attributes (Paper)</title>
<description>@article{12:87,author={Nicol Cesa-Bianchi and Shai Shalev-Shwartz and Ohad Shamir}, Title={Efficient Learning with Partially Observed Attributes},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/cesa-bianchi11a/cesa-bianchi11a.pdf}}</description>
<link>https://academictorrents.com/download/04722103ad77ab5639f019773a828c82183f8f60</link>
</item>
<item>
<title>Graph-Based Hierarchical Conceptual Clustering (Paper)</title>
<description>@article{2:2,author={Istvan Jonyer and Diane J. Cook and Lawrence B. Holder}, Title={Graph-Based Hierarchical Conceptual Clustering},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/jonyer01a/jonyer01a.pdf}}</description>
<link>https://academictorrents.com/download/a874a2fe203f2cce8fb34e3a2cc16b7f6b25fd32</link>
</item>
<item>
<title>The Learning-Curve Sampling Method Applied to Model-Based Clustering (Paper)</title>
<description>@article{2:19,author={Christopher Meek and Bo Thiesson and David Heckerman}, Title={The Learning-Curve Sampling Method Applied to Model-Based Clustering},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/zhang02a/zhang02a.pdf}}</description>
<link>https://academictorrents.com/download/4438c8dba4c0e08576a6cbe1db3ef9ab04c9f922</link>
</item>
<item>
<title>Collaborative Multiagent Reinforcement Learning by Payoff Propagation (Paper)</title>
<description>@article{7:65,author={Jelle R. Kok and Nikos Vlassis}, Title={Collaborative Multiagent Reinforcement Learning by Payoff Propagation},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/kok06a/kok06a.pdf}}</description>
<link>https://academictorrents.com/download/592208652eb5156681acc26f10c6521526830884</link>
</item>
<item>
<title>Adaptive Online Prediction by Following the Perturbed Leader (Paper)</title>
<description>@article{6:22,author={Marcus Hutter and Jan Poland}, Title={Adaptive Online Prediction by Following the Perturbed Leader},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/hutter05a/hutter05a.pdf}}</description>
<link>https://academictorrents.com/download/18f0cf1d83c83b51db477e84fd770d5ac05dbee6</link>
</item>
<item>
<title>The Indian Buffet Process: An Introduction and Review (Paper)</title>
<description>@article{12:32,author={Thomas L. Griffiths and Zoubin Ghahramani}, Title={The Indian Buffet Process: An Introduction and Review},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/griffiths11a/griffiths11a.pdf}}</description>
<link>https://academictorrents.com/download/4590e35ca648f463521828322f1d12393677505e</link>
</item>
<item>
<title>Learning a Robust Relevance Model for Search Using Kernel Methods (Paper)</title>
<description>@article{12:40,author={Wei Wu and Jun Xu and Hang Li and Satoshi Oyama}, Title={Learning a Robust Relevance Model for Search Using Kernel Methods},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/wu11a/wu11a.pdf}}</description>
<link>https://academictorrents.com/download/63c867e896288648b97055456a9bad2c6527c35d</link>
</item>
<item>
<title>Learning Permutations with Exponential Weights (Paper)</title>
<description>@article{10:58,author={David P. Helmbold and Manfred K. Warmuth}, Title={Learning Permutations with Exponential Weights},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/helmbold09a/helmbold09a.pdf}}</description>
<link>https://academictorrents.com/download/371e6a8a2f445e8017eac1f9c94cdc7b60be0524</link>
</item>
<item>
<title>Local Discriminant Wavelet Packet Coordinates for Face Recognition (Paper)</title>
<description>@article{8:42,author={Chao-Chun Liu and Dao-Qing Dai and Hong Yan}, Title={Local Discriminant Wavelet Packet Coordinates for Face Recognition},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/liu07a/liu07a.pdf}}</description>
<link>https://academictorrents.com/download/1ebde5e7722bd9e993343df65704cf777d08f173</link>
</item>
<item>
<title>Learning Transformation Models for Ranking and Survival Analysis (Paper)</title>
<description>@article{12:23,author={Vanya Van Belle and Kristiaan Pelckmans and Johan A. K. Suykens and Sabine Van Huffel}, Title={Learning Transformation Models for Ranking and Survival Analysis},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/vanbelle11a/vanbelle11a.pdf}}</description>
<link>https://academictorrents.com/download/d426f5586e5b5b79155a093659a49e45df66d736</link>
</item>
<item>
<title>Structured Prediction, Dual Extragradient and Bregman Projections (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:60,author={Ben Taskar and Simon Lacoste-Julien and Michael I. Jordan}, Title={Structured Prediction, Dual Extragradient and Bregman Projections (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/taskar06a/taskar06a.pdf}}</description>
<link>https://academictorrents.com/download/161c197fd72aeb5eb6f06162174759bafacb6463</link>
</item>
<item>
<title>Large Margin Semi-supervised Learning (Paper)</title>
<description>@article{8:65,author={Junhui Wang and Xiaotong Shen}, Title={Large Margin Semi-supervised Learning},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/wang07a/wang07a.pdf}}</description>
<link>https://academictorrents.com/download/d9e261b11c763abf68d852e6b6b824a7cca7b6de</link>
</item>
<item>
<title>Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm (Paper)</title>
<description>@article{8:22,author={Markus Kalisch and Peter Bhlmann}, Title={Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/kalisch07a/kalisch07a.pdf}}</description>
<link>https://academictorrents.com/download/9938d5a4bcf4e7986b1059cdb293a5beb65d6233</link>
</item>
<item>
<title>Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets (Paper)</title>
<description>@article{12:47,author={Chiwoo Park and Jianhua Z. Huang and Yu Ding}, Title={Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/park11a/park11a.pdf}}</description>
<link>https://academictorrents.com/download/59b1b88dedf188188da1681cc913c18ca9c97bec</link>
</item>
<item>
<title>Learning from Partial Labels (Paper)</title>
<description>@article{12:42,author={Timothee Cour and Ben Sapp and Ben Taskar}, Title={Learning from Partial Labels},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/cour11a/cour11a.pdf}}</description>
<link>https://academictorrents.com/download/09cf3400cedcc98b277bb073780faa79a0b80474</link>
</item>
<item>
<title>Efficient Structure Learning of Bayesian Networks using Constraints (Paper)</title>
<description>@article{12:20,author={Cassio P. de Campos and Qiang Ji}, Title={Efficient Structure Learning of Bayesian Networks using Constraints},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/decampos11a/decampos11a.pdf}}</description>
<link>https://academictorrents.com/download/b5d0a272f00e853c185784d22b3cb5f4c604b153</link>
</item>
<item>
<title>Generalization Bounds for the Area Under the ROC Curve (Paper)</title>
<description>@article{6:14,author={Shivani Agarwal and Thore Graepel and Ralf Herbrich and Sariel Har-Peled and Dan Roth}, Title={Generalization Bounds for the Area Under the ROC Curve},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/agarwal05a/agarwal05a.pdf}}</description>
<link>https://academictorrents.com/download/325a17176cefb598e7776562e69ef3dfc1fd2480</link>
</item>
<item>
<title>Generalized Bradley-Terry Models and Multi-Class Probability Estimates (Paper)</title>
<description>@article{7:4,author={Tzu-Kuo Huang and Ruby C. Weng and Chih-Jen Lin}, Title={Generalized Bradley-Terry Models and Multi-Class Probability Estimates},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/huang06a/huang06a.pdf}}</description>
<link>https://academictorrents.com/download/19815316b7b4aa16f6abf2bc563a05b995903c22</link>
</item>
<item>
<title>Integrating Nave Bayes and FOIL (Paper)</title>
<description>@article{8:18,author={Niels Landwehr and Kristian Kersting and Luc De Raedt}, Title={Integrating Nave Bayes and FOIL},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/landwehr07a/landwehr07a.pdf}}</description>
<link>https://academictorrents.com/download/8f7ffe6997860f719aed271668c1e1fdc0888647</link>
</item>
<item>
<title>Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models (Paper)</title>
<description>@article{12:68,author={Sharon Goldwater and Thomas L. Griffiths and Mark Johnson}, Title={Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/goldwater11a/goldwater11a.pdf}}</description>
<link>https://academictorrents.com/download/f5c1883a0bbd33e97e7faa573d2acaf2eb34df6e</link>
</item>
<item>
<title>MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals (Paper)</title>
<description>@article{7:7,author={Dana Pe'er and Amos Tanay and Aviv Regev}, Title={MinReg: A Scalable Algorithm for Learning Parsimonious Regulatory Networks in Yeast and Mammals},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/peer06a/peer06a.pdf}}</description>
<link>https://academictorrents.com/download/bb33c60ea59f55679d51c8070c51206265b93b09</link>
</item>
<item>
<title>Computationally Efficient Convolved Multiple Output Gaussian Processes (Paper)</title>
<description>@article{12:41,author={Mauricio A. lvarez and Neil D. Lawrence}, Title={Computationally Efficient Convolved Multiple Output Gaussian Processes},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/alvarez11a/alvarez11a.pdf}}</description>
<link>https://academictorrents.com/download/8229e8964303fb5bb9e62ed070b79d1e2198a945</link>
</item>
<item>
<title>Polynomial Identification in the Limit of Substitutable Context-free Languages (Paper)</title>
<description>@article{8:60,author={Alexander Clark and Rmi Eyraud}, Title={Polynomial Identification in the Limit of Substitutable Context-free Languages},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/clark07a/clark07a.pdf}}</description>
<link>https://academictorrents.com/download/8582796ce77f239d88f9d65e026c7bf64f7ce642</link>
</item>
<item>
<title>Locally Defined Principal Curves and Surfaces (Paper)</title>
<description>@article{12:34,author={Umut Ozertem and Deniz Erdogmus}, Title={Locally Defined Principal Curves and Surfaces},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/ozertem11a/ozertem11a.pdf}}</description>
<link>https://academictorrents.com/download/ca245e9710f3d0d8205c714dc2380642e0f1c879</link>
</item>
<item>
<title>Laplacian Support Vector Machines Trained in the Primal (Paper)</title>
<description>@article{12:31,author={Stefano Melacci and Mikhail Belkin}, Title={Laplacian Support Vector Machines  Trained in the Primal},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/melacci11a/melacci11a.pdf}}</description>
<link>https://academictorrents.com/download/ca13f9759a4a9c77cb3eac5d644bd6e63976369f</link>
</item>
<item>
<title>Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach (Special Topic on Inductive Programming) (Paper)</title>
<description>@article{7:15,author={Emanuel Kitzelmann and Ute Schmid}, Title={Inductive Synthesis of Functional Programs: An Explanation Based Generalization Approach (Special Topic on Inductive Programming)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/kitzelmann06a/kitzelmann06a.pdf}}</description>
<link>https://academictorrents.com/download/9892859c9259965676386476680f11f1f59976de</link>
</item>
<item>
<title>Introduction to Special Issue on Machine Learning Approaches to Shallow Parsing (Paper)</title>
<description>@article{2:24,author={James Hammerton and Miles Osborne and Susan Armstrong and Walter Daelemans}, Title={Introduction to Special Issue on Machine Learning Approaches to Shallow Parsing},journal={Journal of Machine Learning Research},volume={2}, url={http://www.jmlr.org/papers/volume2/chickering02a/chickering02a.pdf}}</description>
<link>https://academictorrents.com/download/62e4b0e2c43b84917900a16ba787a0bfe39d1101</link>
</item>
<item>
<title>Kernel-Based Learning of Hierarchical Multilabel Classification Models (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:59,author={Juho Rousu and Craig Saunders and Sandor Szedmak and John Shawe-Taylor}, Title={Kernel-Based Learning of Hierarchical Multilabel Classification Models (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/rousu06a/rousu06a.pdf}}</description>
<link>https://academictorrents.com/download/94712fb735291e85dc0ba56754845a08d5fff917</link>
</item>
<item>
<title>Stability Properties of Empirical Risk Minimization over Donsker Classes (Paper)</title>
<description>@article{7:91,author={Andrea Caponnetto and Alexander Rakhlin}, Title={Stability Properties of Empirical Risk Minimization over Donsker Classes},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/caponnetto06a/caponnetto06a.pdf}}</description>
<link>https://academictorrents.com/download/164dfc2054da248f85722945ffb0777ddc89eddc</link>
</item>
<item>
<title>Inverse Reinforcement Learning in Partially Observable Environments (Paper)</title>
<description>@article{12:21,author={Jaedeug Choi and Kee-Eung Kim}, Title={Inverse Reinforcement Learning in Partially Observable Environments},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/choi11a/choi11a.pdf}}</description>
<link>https://academictorrents.com/download/3b46631410d926a0424de8036eadedd16773e1a5</link>
</item>
<item>
<title>Building Blocks for Variational Bayesian Learning of Latent Variable Models (Paper)</title>
<description>@article{8:6,author={Tapani Raiko and Harri Valpola and Markus Harva and Juha Karhunen}, Title={Building Blocks for Variational Bayesian Learning of Latent Variable Models},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/raiko07a/raiko07a.pdf}}</description>
<link>https://academictorrents.com/download/b1637b1db97ed934c05b61b6aed29743647cb76c</link>
</item>
<item>
<title>Hierarchical Average Reward Reinforcement Learning (Paper)</title>
<description>@article{8:87,author={Mohammad Ghavamzadeh and Sridhar Mahadevan}, Title={Hierarchical Average Reward Reinforcement Learning},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/ghavamzadeh07a/ghavamzadeh07a.pdf}}</description>
<link>https://academictorrents.com/download/a15142ab25e34c2e5b3af80186f528305070880a</link>
</item>
<item>
<title>Large Margin Hierarchical Classification with Mutually Exclusive Class Membership (Paper)</title>
<description>@article{12:82,author={Huixin Wang and Xiaotong Shen and Wei Pan}, Title={Large Margin Hierarchical Classification with Mutually Exclusive Class Membership},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/wang11c/wang11c.pdf}}</description>
<link>https://academictorrents.com/download/c81e1a1ae19abcf4bbb3a51fe6134778fd0d1985</link>
</item>
<item>
<title>Causal Graph Based Decomposition of Factored MDPs (Paper)</title>
<description>@article{7:81,author={Anders Jonsson and Andrew Barto}, Title={Causal Graph Based Decomposition of Factored MDPs},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/jonsson06a/jonsson06a.pdf}}</description>
<link>https://academictorrents.com/download/386834e90cfa8d286a02e1aea2009b56f6a01ff0</link>
</item>
<item>
<title>MSVMpack: A Multi-Class Support Vector Machine Package (Paper)</title>
<description>@article{12:66,author={Fabien Lauer and Yann Guermeur}, Title={MSVMpack: A Multi-Class Support Vector Machine Package},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/lauer11a/lauer11a.pdf}}</description>
<link>https://academictorrents.com/download/33ae95873171c2644aebd2858ef7da5c792c9262</link>
</item>
<item>
<title>Training SVMs Without Offset (Paper)</title>
<description>@article{12:6,author={Ingo Steinwart and Don Hush and Clint Scovel}, Title={Training SVMs Without Offset},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/steinwart11a/steinwart11a.pdf}}</description>
<link>https://academictorrents.com/download/ae48285c025e30986bbe68e197863f4857690b5b</link>
</item>
<item>
<title>Bounded Kernel-Based Online Learning (Paper)</title>
<description>@article{10:92,author={Francesco Orabona and Joseph Keshet and Barbara Caputo}, Title={Bounded Kernel-Based Online Learning},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/orabona09a/orabona09a.pdf}}</description>
<link>https://academictorrents.com/download/2aa2212325162e2d22c8fbb2790fb1b5d30fe275</link>
</item>
<item>
<title>Learning Parts-Based Representations of Data (Paper)</title>
<description>@article{7:84,author={David A. Ross and Richard S. Zemel}, Title={Learning Parts-Based Representations of Data},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/ross06a/ross06a.pdf}}</description>
<link>https://academictorrents.com/download/0357c6b615807429d48a6e0d661a77fbef1ad122</link>
</item>
<item>
<title>Semi-Supervised Learning with Measure Propagation (Paper)</title>
<description>@article{12:102,author={Amarnag Subramanya and Jeff Bilmes}, Title={Semi-Supervised Learning with Measure Propagation},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/subramanya11a/subramanya11a.pdf}}</description>
<link>https://academictorrents.com/download/7400e484c2bdc03641f6e277f868abee8f37f56f</link>
</item>
<item>
<title>Convex and Network Flow Optimization for Structured Sparsity (Paper)</title>
<description>@article{12:81,author={Julien Mairal and Rodolphe Jenatton and Guillaume Obozinski and Francis Bach}, Title={Convex and Network Flow Optimization for Structured Sparsity},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/mairal11a/mairal11a.pdf}}</description>
<link>https://academictorrents.com/download/3c3a188d4c891f1dc8aa40ef2140e88b63efe684</link>
</item>
<item>
<title>LPmade: Link Prediction Made Easy (Paper)</title>
<description>@article{12:75,author={Ryan N. Lichtenwalter and Nitesh V. Chawla}, Title={LPmade: Link Prediction Made Easy},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/lichtenwalter11a/lichtenwalter11a.pdf}}</description>
<link>https://academictorrents.com/download/8b1578efd4408a6f87ee11704b6b0077763cab40</link>
</item>
<item>
<title>Truncating the Loop Series Expansion for Belief Propagation (Paper)</title>
<description>@article{8:68,author={Vicen Gmez and Joris M. Mooij and Hilbert J. Kappen}, Title={Truncating the Loop Series Expansion for Belief Propagation},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/gomez07a/gomez07a.pdf}}</description>
<link>https://academictorrents.com/download/8a7a6032b5ba4accb6e64faf9a6d368df80654db</link>
</item>
<item>
<title>A Hierarchy of Support Vector Machines for Pattern Detection (Paper)</title>
<description>@article{7:75,author={Hichem Sahbi and Donald Geman}, Title={A Hierarchy of Support Vector Machines for Pattern Detection},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/sahbi06a/sahbi06a.pdf}}</description>
<link>https://academictorrents.com/download/fdc712a3fbac1b24a3abbe391f720fcf68409a7b</link>
</item>
<item>
<title>The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets (Paper)</title>
<description>@article{12:57,author={Michael Hahsler and Sudheer Chelluboina and Kurt Hornik and Christian Buchta}, Title={The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/hahsler11a/hahsler11a.pdf}}</description>
<link>https://academictorrents.com/download/7e5c3ac40b1b0bea28dcaa6cb5e9c05e5b3a68ef</link>
</item>
<item>
<title>Proximal Methods for Hierarchical Sparse Coding (Paper)</title>
<description>@article{12:67,author={Rodolphe Jenatton and Julien Mairal and Guillaume Obozinski and Francis Bach}, Title={Proximal Methods for Hierarchical Sparse Coding},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/jenatton11a/jenatton11a.pdf}}</description>
<link>https://academictorrents.com/download/d0d17069650e8d58979b4a2230c9c4ceab89323d</link>
</item>
<item>
<title>A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin (Paper)</title>
<description>@article{12:51,author={Liwei Wang and Masashi Sugiyama and Zhaoxiang Jing and Cheng Yang and Zhi-Hua Zhou and Jufu Feng}, Title={A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/wang11a/wang11a.pdf}}</description>
<link>https://academictorrents.com/download/9df16aa64cc9fd65e8b97b8c49824c021dfd18fd</link>
</item>
<item>
<title>Concave Learners for Rankboost (Paper)</title>
<description>@article{8:29,author={Ofer Melnik and Yehuda Vardi and Cun-Hui Zhang}, Title={Concave Learners for Rankboost},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/melnik07a/melnik07a.pdf}}</description>
<link>https://academictorrents.com/download/c27abdf703fdc5f4ae00779f3cd94aaa1a9fabf9</link>
</item>
<item>
<title>Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data (Paper)</title>
<description>@article{12:27,author={Zeeshan Syed and John Guttag}, Title={Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/syed11a/syed11a.pdf}}</description>
<link>https://academictorrents.com/download/645268cc6d67bed015329f662a1fe0728cb50fcb</link>
</item>
<item>
<title>In Search of Non-Gaussian Components of a High-Dimensional Distribution (Paper)</title>
<description>@article{7:9,author={Gilles Blanchard and Motoaki Kawanabe and Masashi Sugiyama and Vladimir Spokoiny and Klaus-Robert Mller}, Title={In Search of Non-Gaussian Components of a High-Dimensional Distribution},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/blanchard06a/blanchard06a.pdf}}</description>
<link>https://academictorrents.com/download/10a165ce9747da34cb29632b9a5cce7912fd8ddc</link>
</item>
<item>
<title>Asymptotics in Empirical Risk Minimization (Paper)</title>
<description>@article{6:68,author={Leila Mohammadi and Sara van de Geer}, Title={Asymptotics in Empirical Risk Minimization},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/mohammadi05a/mohammadi05a.pdf}}</description>
<link>https://academictorrents.com/download/b8cfa79adb8e35287c9ace14cecceb964cb808b0</link>
</item>
<item>
<title>Neyman-Pearson Classification, Convexity and Stochastic Constraints (Paper)</title>
<description>@article{12:86,author={Philippe Rigollet and Xin Tong}, Title={Neyman-Pearson Classification, Convexity and Stochastic Constraints},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/rigollet11a/rigollet11a.pdf}}</description>
<link>https://academictorrents.com/download/c155687e5522ae11e5ec2d018a96e0c27a8d9acc</link>
</item>
<item>
<title>Generalized TD Learning (Paper)</title>
<description>@article{12:56,author={Tsuyoshi Ueno and Shin-ichi Maeda and Motoaki Kawanabe and Shin Ishii}, Title={Generalized TD Learning},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/ueno11a/ueno11a.pdf}}</description>
<link>https://academictorrents.com/download/7f095755a42c38cd1dfa2f08bcd8a6371249cc5e</link>
</item>
<item>
<title>Walk-Sums and Belief Propagation in Gaussian Graphical Models (Paper)</title>
<description>@article{7:73,author={Dmitry M. Malioutov and Jason K. Johnson and Alan S. Willsky}, Title={Walk-Sums and Belief Propagation in Gaussian Graphical Models},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/malioutov06a/malioutov06a.pdf}}</description>
<link>https://academictorrents.com/download/0f7ad02c15111c41922fc6f445fda383f760ce09</link>
</item>
<item>
<title>Building Support Vector Machines with Reduced Classifier Complexity (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:55,author={S. Sathiya Keerthi and Olivier Chapelle and Dennis DeCoste}, Title={Building Support Vector Machines with Reduced Classifier Complexity (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/keerthi06a/keerthi06a.pdf}}</description>
<link>https://academictorrents.com/download/f91fa40ea6d17f2fa2b020ff21838ae5a8fef79b</link>
</item>
<item>
<title>QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines (Paper)</title>
<description>@article{7:26,author={Don Hush and Patrick Kelly and Clint Scovel and Ingo Steinwart}, Title={QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/hush06a/hush06a.pdf}}</description>
<link>https://academictorrents.com/download/2aa615cad7ebba3dab42edf5f6a62fb805fcf28d</link>
</item>
<item>
<title>"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks (Paper)</title>
<description>@article{8:63,author={Gal Elidan and Iftach Nachman and Nir Friedman}, Title={"Ideal Parent" Structure Learning for Continuous Variable Bayesian Networks},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/elidan07a/elidan07a.pdf}}</description>
<link>https://academictorrents.com/download/2ad4d685df8fdcd5f6b735fd3a0e8295f0501b47</link>
</item>
<item>
<title>Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:54,author={Luca Zanni and Thomas Serafini and Gaetano Zanghirati}, Title={Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/zanni06a/zanni06a.pdf}}</description>
<link>https://academictorrents.com/download/cd35083d3efcbc4eb0212f92831346b724f204d1</link>
</item>
<item>
<title>Learning Factor Graphs in Polynomial Time and Sample Complexity (Paper)</title>
<description>@article{7:64,author={Pieter Abbeel and Daphne Koller and Andrew Y. Ng}, Title={Learning Factor Graphs in Polynomial Time and Sample Complexity},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/abbeel06a/abbeel06a.pdf}}</description>
<link>https://academictorrents.com/download/06e17519271a6492d76f0a59189a7ee7f5488a2c</link>
</item>
<item>
<title>Point-Based Value Iteration for Continuous POMDPs (Paper)</title>
<description>@article{7:83,author={Josep M. Porta and Nikos Vlassis and Matthijs T.J. Spaan and Pascal Poupart}, Title={Point-Based Value Iteration for Continuous POMDPs},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/porta06a/porta06a.pdf}}</description>
<link>https://academictorrents.com/download/87ef9ab0ffb6ce89a2f2b4893fce0e822aea1ec3</link>
</item>
<item>
<title>A Graphical Representation of Equivalence Classes of AMP Chain Graphs (Paper)</title>
<description>@article{7:38,author={Alberto Roverato and Milan Studen}, Title={A Graphical Representation of Equivalence Classes of AMP Chain Graphs},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/roverato06a/roverato06a.pdf}}</description>
<link>https://academictorrents.com/download/39c2fdfdd24673eead933a415f84a238bbdb8273</link>
</item>
<item>
<title>Segmental Hidden Markov Models with Random Effects for Waveform Modeling (Paper)</title>
<description>@article{7:33,author={Seyoung Kim and Padhraic Smyth}, Title={Segmental Hidden Markov Models with Random Effects for Waveform Modeling},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/kim06a/kim06a.pdf}}</description>
<link>https://academictorrents.com/download/590e4885c3053387cb96a1322d74adead690b025</link>
</item>
<item>
<title>Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates (Paper)</title>
<description>@article{12:45,author={Vincent Y.F. Tan and Animashree Anandkumar and Alan S. Willsky}, Title={Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/tan11a/tan11a.pdf}}</description>
<link>https://academictorrents.com/download/2323614427d18622693c0bc286ed28341735f35e</link>
</item>
<item>
<title>Noisy-OR Component Analysis and its Application to Link Analysis (Paper)</title>
<description>@article{7:78,author={Tom ingliar and Milo Hauskrecht}, Title={Noisy-OR Component Analysis and its Application to Link Analysis},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/singliar06a/singliar06a.pdf}}</description>
<link>https://academictorrents.com/download/d07347babdc3166470e0d5a755d7533a5cf8adb0</link>
</item>
<item>
<title>Online Learning of Multiple Tasks with a Shared Loss (Paper)</title>
<description>@article{8:75,author={Ofer Dekel and Philip M. Long and Yoram Singer}, Title={Online Learning of Multiple Tasks with a Shared Loss},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/dekel07a/dekel07a.pdf}}</description>
<link>https://academictorrents.com/download/ac5ee290188aea1123fc1316b8103da17d8c253a</link>
</item>
<item>
<title>Maximum-Gain Working Set Selection for SVMs (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:53,author={Tobias Glasmachers and Christian Igel}, Title={Maximum-Gain Working Set Selection for SVMs (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/glasmachers06a/glasmachers06a.pdf}}</description>
<link>https://academictorrents.com/download/e0bd2a8edfc23f87a1076cfe42967ac99236947a</link>
</item>
<item>
<title>Logistic Stick-Breaking Process (Paper)</title>
<description>@article{12:7,author={Lu Ren and Lan Du and Lawrence Carin and David Dunson}, Title={Logistic Stick-Breaking Process},journal={Journal of Machine Learning Research},volume={12}, url={http://www.jmlr.org/papers/volume12/ren11a/ren11a.pdf}}</description>
<link>https://academictorrents.com/download/1b0d420bb3d183804b33d20aca0df7dd77c745c8</link>
</item>
<item>
<title>Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:52,author={Tijl De Bie and Nello Cristianini}, Title={Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/debie06a/debie06a.pdf}}</description>
<link>https://academictorrents.com/download/1fae6dbfbf47b107eb5aacd990d57c63431ad610</link>
</item>
<item>
<title>Sparse Boosting (Paper)</title>
<description>@article{7:36,author={Peter Bhlmann and Bin Yu}, Title={Sparse Boosting},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/buehlmann06a/buehlmann06a.pdf}}</description>
<link>https://academictorrents.com/download/c3cc042283428b9fce9114e881968a362fd71d56</link>
</item>
<item>
<title>Consistency and Convergence Rates of One-Class SVMs and Related Algorithms (Paper)</title>
<description>@article{7:29,author={Rgis Vert and Jean-Philippe Vert}, Title={Consistency and Convergence Rates of One-Class SVMs and Related Algorithms},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/vert06a/vert06a.pdf}}</description>
<link>https://academictorrents.com/download/0490e9e2abbba7c1f79d8f9ce5e671ec08abe061</link>
</item>
<item>
<title>Synergistic Face Detection and Pose Estimation with Energy-Based Models (Paper)</title>
<description>@article{8:43,author={Margarita Osadchy and Yann Le Cun and Matthew L. Miller}, Title={Synergistic Face Detection and Pose Estimation with Energy-Based Models},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/osadchy07a/osadchy07a.pdf}}</description>
<link>https://academictorrents.com/download/78d2322a3b1b84e7de747110d9d271c918b32496</link>
</item>
<item>
<title>Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation(Special Topic on Causality) (Paper)</title>
<description>@article{10:12,author={Facundo Bromberg and Dimitris Margaritis}, Title={Improving the Reliability of Causal Discovery from Small Data Sets Using Argumentation(Special Topic on Causality)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/bromberg09a/bromberg09a.pdf}}</description>
<link>https://academictorrents.com/download/d98191e0b45edf22fa510113fb6315156c371d16</link>
</item>
<item>
<title>Covariate Shift Adaptation by Importance Weighted Cross Validation (Paper)</title>
<description>@article{8:35,author={Masashi Sugiyama and Matthias Krauledat and Klaus-Robert Mller}, Title={Covariate Shift Adaptation by Importance Weighted Cross Validation},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/sugiyama07a/sugiyama07a.pdf}}</description>
<link>https://academictorrents.com/download/633416b143a9e6ef89d9fb138179a72712a5bedf</link>
</item>
<item>
<title>Fourier Theoretic Probabilistic Inference over Permutations (Paper)</title>
<description>@article{10:37,author={Jonathan Huang and Carlos Guestrin and Leonidas Guibas}, Title={Fourier Theoretic Probabilistic Inference over Permutations},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/huang09a/huang09a.pdf}}</description>
<link>https://academictorrents.com/download/d9c86f4df1aa7b0173662a5a7d8058eca5942ca8</link>
</item>
<item>
<title>Second Order Cone Programming Approaches for Handling Missing and Uncertain Data (Special Topic on Machine Learning and Optimization) (Paper)</title>
<description>@article{7:47,author={Pannagadatta K. Shivaswamy and Chiranjib Bhattacharyya and Alexander J. Smola}, Title={Second Order Cone Programming Approaches for Handling Missing and Uncertain Data (Special Topic on Machine Learning and Optimization)},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/shivaswamy06a/shivaswamy06a.pdf}}</description>
<link>https://academictorrents.com/download/9135afd552fbbae3cffb883294da5b29bad5feb0</link>
</item>
<item>
<title>Particle Swarm Model Selection(Special Topic on Model Selection) (Paper)</title>
<description>@article{10:15,author={Hugo Jair Escalante and Manuel Montes and Luis Enrique Sucar}, Title={Particle Swarm Model Selection(Special Topic on Model Selection)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/escalante09a/escalante09a.pdf}}</description>
<link>https://academictorrents.com/download/c0d6ae7d64439abec77021312c7fa9fff0b7a063</link>
</item>
<item>
<title>Streamwise Feature Selection (Paper)</title>
<description>@article{7:67,author={Jing Zhou and Dean P. Foster and Robert A. Stine and Lyle H. Ungar}, Title={Streamwise Feature Selection},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/zhou06a/zhou06a.pdf}}</description>
<link>https://academictorrents.com/download/ffb340ed10e7cc97d2bb31a5ebc61d47216d02cf</link>
</item>
<item>
<title>Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems (Paper)</title>
<description>@article{7:39,author={Eyal Even-Dar and Shie Mannor and Yishay Mansour}, Title={Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/evendar06a/evendar06a.pdf}}</description>
<link>https://academictorrents.com/download/e55591692712e84ec086b796c930d5182b77c7bc</link>
</item>
<item>
<title>Approximating the Permanent with Fractional Belief Propagation (Paper)</title>
<description>@article{14:63,author={Michael Chertkov and Adam B. Yedidia}, Title={Approximating the Permanent with Fractional Belief Propagation},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/chertkov13a/chertkov13a.pdf}}</description>
<link>https://academictorrents.com/download/2dace21ae42a298afec2e0a205257bf94b7a22d7</link>
</item>
<item>
<title>Learning the Structure of Linear Latent Variable Models (Paper)</title>
<description>@article{7:8,author={Ricardo Silva and Richard Scheine and Clark Glymour and Peter Spirtes}, Title={Learning the Structure of Linear Latent Variable Models},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/silva06a/silva06a.pdf}}</description>
<link>https://academictorrents.com/download/4e3120adac0a09818bca00485cc0e5511b9cdaca</link>
</item>
<item>
<title>Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood (Paper)</title>
<description>@article{14:3,author={Jaakko Riihimki and Pasi Jylnki and Aki Vehtari}, Title={Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/riihimaki13a/riihimaki13a.pdf}}</description>
<link>https://academictorrents.com/download/e4f58dc83c8dff5c08b8faac05907c971b4f9d25</link>
</item>
<item>
<title>Using Machine Learning to Guide Architecture Simulation (Paper)</title>
<description>@article{7:12,author={Greg Hamerly and Erez Perelman and Jeremy Lau and Brad Calder and Timothy Sherwood}, Title={Using Machine Learning to Guide Architecture Simulation},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/hamerly06a/hamerly06a.pdf}}</description>
<link>https://academictorrents.com/download/3405e560f80ca053f80116b7e3fef17583f2e846</link>
</item>
<item>
<title>On the Complexity of Learning Lexicographic Strategies (Paper)</title>
<description>@article{7:3,author={Michael Schmitt and Laura Martignon}, Title={On the Complexity of Learning Lexicographic Strategies},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/schmitt06a/schmitt06a.pdf}}</description>
<link>https://academictorrents.com/download/892b58aa3a2bf66f9b96294f4f5124b9785d7243</link>
</item>
<item>
<title>Combining PAC-Bayesian and Generic Chaining Bounds (Paper)</title>
<description>@article{8:32,author={Jean-Yves Audibert and Olivier Bousquet}, Title={Combining PAC-Bayesian and Generic Chaining Bounds},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/audibert07a/audibert07a.pdf}}</description>
<link>https://academictorrents.com/download/a65b847b6fdd44bf84a6a7c5852c30251efcabe8</link>
</item>
<item>
<title>The Pyramid Match Kernel: Efficient Learning with Sets of Features (Paper)</title>
<description>@article{8:26,author={Kristen Grauman and Trevor Darrell}, Title={The Pyramid Match Kernel: Efficient Learning with Sets of Features},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/grauman07a/grauman07a.pdf}}</description>
<link>https://academictorrents.com/download/ac831f277885bb169067d73f660e092361daa8ec</link>
</item>
<item>
<title>Reproducing Kernel Banach Spaces for Machine Learning (Paper)</title>
<description>@article{10:95,author={Haizhang Zhang and Yuesheng Xu and Jun Zhang}, Title={Reproducing Kernel Banach Spaces for Machine Learning},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/zhang09b/zhang09b.pdf}}</description>
<link>https://academictorrents.com/download/d357547afa430de702b98cf13e85225b11c697d4</link>
</item>
<item>
<title>Active Coevolutionary Learning of Deterministic Finite Automata (Paper)</title>
<description>@article{6:56,author={Josh Bongard and Hod Lipson}, Title={Active Coevolutionary Learning of Deterministic Finite Automata},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/bongard05a/bongard05a.pdf}}</description>
<link>https://academictorrents.com/download/292679736beef01ce8ac8e6e69069d8cbeef2bf5</link>
</item>
<item>
<title>Multi-class Protein Classification Using Adaptive Codes (Paper)</title>
<description>@article{8:55,author={Iain Melvin and Eugene Ie and Jason Weston and William Stafford Noble and Christina Leslie}, Title={Multi-class Protein Classification Using Adaptive Codes},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/melvin07a/melvin07a.pdf}}</description>
<link>https://academictorrents.com/download/a674b3f64e8c7cfd1949ba8e5f76726c6f0f5382</link>
</item>
<item>
<title>Maximum Volume Clustering: A New Discriminative Clustering Approach (Paper)</title>
<description>@article{14:82,author={Gang Niu and Bo Dai and Lin Shang and Masashi Sugiyama}, Title={Maximum Volume Clustering: A New Discriminative Clustering Approach},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/niu13a/niu13a.pdf}}</description>
<link>https://academictorrents.com/download/56b59a7aca9c896a2b318b965880702cba7ca4eb</link>
</item>
<item>
<title>Minimax Regret Classifier for Imprecise Class Distributions (Paper)</title>
<description>@article{8:4,author={Roco Alaiz-Rodrguez and Alicia Guerrero-Curieses and Jess Cid-Sueiro}, Title={Minimax Regret Classifier for Imprecise Class Distributions},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/alaiz-rodriguez07a/alaiz-rodriguez07a.pdf}}</description>
<link>https://academictorrents.com/download/5d539a6f725c0f012fe158f786b1238d9e92a4a8</link>
</item>
<item>
<title>Transfer Learning via Inter-Task Mappings for Temporal Difference Learning (Paper)</title>
<description>@article{8:73,author={Matthew E. Taylor and Peter Stone and Yaxin Liu}, Title={Transfer Learning via Inter-Task Mappings for Temporal Difference Learning},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/taylor07a/taylor07a.pdf}}</description>
<link>https://academictorrents.com/download/1e77af32aaf43c32b30406d455c3ba4f85585024</link>
</item>
<item>
<title>Relational Dependency Networks (Paper)</title>
<description>@article{8:24,author={Jennifer Neville and David Jensen}, Title={Relational Dependency Networks},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/neville07a/neville07a.pdf}}</description>
<link>https://academictorrents.com/download/a970d35f66fb5c847726dccbba20859157f0f6f3</link>
</item>
<item>
<title>Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach (Paper)</title>
<description>@article{6:62,author={Lior Wolf and Amnon Shashua}, Title={Feature Selection for Unsupervised and Supervised Inference: The Emergence of Sparsity in a Weight-Based Approach},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/wolf05a/wolf05a.pdf}}</description>
<link>https://academictorrents.com/download/8759bd084281cf78c6045dc3191e47666a6923bc</link>
</item>
<item>
<title>Online Passive-Aggressive Algorithms (Paper)</title>
<description>@article{7:19,author={Koby Crammer and Ofer Dekel and Joseph Keshet and Shai Shalev-Shwartz and Yoram Singer}, Title={Online Passive-Aggressive Algorithms},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/crammer06a/crammer06a.pdf}}</description>
<link>https://academictorrents.com/download/8203bc0f1ce7b42b06537d78b8b1315154813abb</link>
</item>
<item>
<title>Kernel Methods for Measuring Independence (Paper)</title>
<description>@article{6:70,author={Arthur Gretton and Ralf Herbrich and Alexander Smola and Olivier Bousquet and Bernhard Schlkopf}, Title={Kernel Methods for Measuring Independence},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/gretton05a/gretton05a.pdf}}</description>
<link>https://academictorrents.com/download/931d10c8b632338ea2b30f6c749ff934ccf4c7a0</link>
</item>
<item>
<title>Behavioral Shaping for Geometric Concepts (Paper)</title>
<description>@article{8:64,author={Manu Chhabra and Robert A. Jacobs and Daniel tefankovi}, Title={Behavioral Shaping for Geometric Concepts},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/chhabra07a/chhabra07a.pdf}}</description>
<link>https://academictorrents.com/download/8acaba10076f9619a985ccc393b7b2c004ff9cb3</link>
</item>
<item>
<title>Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes (Paper)</title>
<description>@article{8:74,author={Sridhar Mahadevan and Mauro Maggioni}, Title={Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/mahadevan07a/mahadevan07a.pdf}}</description>
<link>https://academictorrents.com/download/cca65715cb31b53876c27c0f4ddcd2be9ad7036a</link>
</item>
<item>
<title>Undercomplete Blind Subspace Deconvolution (Paper)</title>
<description>@article{8:38,author={Zoltn Szab and Barnabs Pczos and Andrs Lrincz}, Title={Undercomplete Blind Subspace Deconvolution},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/szabo07a/szabo07a.pdf}}</description>
<link>https://academictorrents.com/download/640ed853870355189e3727bbe0ae431844cf69c2</link>
</item>
<item>
<title>Joint Harmonic Functions and Their Supervised Connections (Paper)</title>
<description>@article{14:118,author={Mark Vere Culp and Kenneth Joseph Ryan}, Title={Joint Harmonic Functions and Their Supervised Connections},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/culp13a/culp13a.pdf}}</description>
<link>https://academictorrents.com/download/baa7a77af59e0094b7e8cf7f8b63b712482b2a04</link>
</item>
<item>
<title>A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation (Paper)</title>
<description>@article{8:67,author={Arindam Banerjee and Inderjit Dhillon and Joydeep Ghosh and Srujana Merugu and Dharmendra S. Modha}, Title={A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/banerjee07a/banerjee07a.pdf}}</description>
<link>https://academictorrents.com/download/283544b0bfa9937de05b7d11ea6de7798d85bf14</link>
</item>
<item>
<title>A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians (Paper)</title>
<description>@article{8:7,author={Sanjoy Dasgupta and Leonard Schulman}, Title={A Probabilistic Analysis of EM for Mixtures of Separated, Spherical Gaussians},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/dasgupta07a/dasgupta07a.pdf}}</description>
<link>https://academictorrents.com/download/add36f9231e0ff84311f823e9ff7149eff456fb2</link>
</item>
<item>
<title>Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions (Paper)</title>
<description>@article{6:44,author={Peter Binev and Albert Cohen and Wolfgang Dahmen and Ronald DeVore and Vladimir Temlyakov}, Title={Universal Algorithms for Learning Theory Part I : Piecewise Constant Functions},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/binev05a/binev05a.pdf}}</description>
<link>https://academictorrents.com/download/7ff1031a0652dc89df2e421e96f5db3e329b295f</link>
</item>
<item>
<title>The On-Line Shortest Path Problem Under Partial Monitoring (Paper)</title>
<description>@article{8:79,author={Andrs Gyrgy and Tams Linder and Gbor Lugosi and Gyrgy Ottucsk}, Title={The On-Line Shortest Path Problem Under Partial Monitoring},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/gyoergy07a/gyoergy07a.pdf}}</description>
<link>https://academictorrents.com/download/4bbf7327eb4fa9d8b3c5446ec672b47ca1ee7930</link>
</item>
<item>
<title>Loopy Belief Propagation: Convergence and Effects of Message Errors (Paper)</title>
<description>@article{6:31,author={Alexander T. Ihler and John W. Fisher III and Alan S. Willsky}, Title={Loopy Belief Propagation: Convergence and Effects of Message Errors},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/ihler05a/ihler05a.pdf}}</description>
<link>https://academictorrents.com/download/ee3ee33ffc7f2f65940e4f7f7071c3335eb70af2</link>
</item>
<item>
<title>Change Point Problems in Linear Dynamical Systems (Paper)</title>
<description>@article{6:67,author={Onno Zoeter and Tom Heskes}, Title={Change Point Problems in Linear Dynamical Systems},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/zoeter05a/zoeter05a.pdf}}</description>
<link>https://academictorrents.com/download/2b6280b26e86beb1b4f456224dea97ceed2fd78e</link>
</item>
<item>
<title>Fast Kernel Classifiers with Online and Active Learning (Paper)</title>
<description>@article{6:54,author={Antoine Bordes and Seyda Ertekin and Jason Weston and Lon Bottou}, Title={Fast Kernel Classifiers with Online and Active Learning},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/bordes05a/bordes05a.pdf}}</description>
<link>https://academictorrents.com/download/e19fecddcaf73540fc5e63d10d36b964763ae0c2</link>
</item>
<item>
<title>Convergence Theorems for Generalized Alternating Minimization Procedures (Paper)</title>
<description>@article{6:69,author={Asela Gunawardana and William Byrne}, Title={Convergence Theorems for Generalized Alternating Minimization Procedures},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/gunawardana05a/gunawardana05a.pdf}}</description>
<link>https://academictorrents.com/download/ad9358933c47d5b4862f97d9a0788346d3c92283</link>
</item>
<item>
<title>Spherical-Homoscedastic Distributions: The Equivalency of Spherical and Normal Distributions in Classification (Paper)</title>
<description>@article{8:56,author={Onur C. Hamsici and Aleix M. Martinez}, Title={Spherical-Homoscedastic Distributions: The Equivalency of Spherical and Normal Distributions in Classification},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/hamsici07a/hamsici07a.pdf}}</description>
<link>https://academictorrents.com/download/17aad516d43bf716616f072927901baef114b835</link>
</item>
<item>
<title>Learnability of Gaussians with Flexible Variances (Paper)</title>
<description>@article{8:9,author={Yiming Ying and Ding-Xuan Zhou}, Title={Learnability of Gaussians with Flexible Variances},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/ying07a/ying07a.pdf}}</description>
<link>https://academictorrents.com/download/324053932b051500ed6697786468f0a883adc498</link>
</item>
<item>
<title>Algorithms and Hardness Results for Parallel Large Margin Learning (Paper)</title>
<description>@article{14:96,author={Philip M. Long and Rocco A. Servedio}, Title={Algorithms and Hardness Results for Parallel Large Margin Learning},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/long13a/long13a.pdf}}</description>
<link>https://academictorrents.com/download/1c4b00397b095763f3dd14fb916b5dd391c485f6</link>
</item>
<item>
<title>Regularization-Free Principal Curve Estimation (Paper)</title>
<description>@article{14:40,author={Samuel Gerber and Ross Whitaker}, Title={Regularization-Free Principal Curve Estimation},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/gerber13a/gerber13a.pdf}}</description>
<link>https://academictorrents.com/download/b506cdd629ea8c0c340cf02b46645a8ebc675ef8</link>
</item>
<item>
<title>Toward Attribute Efficient Learning of Decision Lists and Parities (Paper)</title>
<description>@article{7:20,author={Adam R. Klivans and Rocco A. Servedio}, Title={Toward Attribute Efficient Learning of Decision Lists and Parities},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/klivans06a/klivans06a.pdf}}</description>
<link>https://academictorrents.com/download/2edbe151e1ba2a0c63b6c4367849e26cb2f6f21c</link>
</item>
<item>
<title>Measuring Differentiability: Unmasking Pseudonymous Authors (Paper)</title>
<description>@article{8:45,author={Moshe Koppel and Jonathan Schler and Elisheva Bonchek-Dokow}, Title={Measuring Differentiability:  Unmasking Pseudonymous Authors},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/koppel07a/koppel07a.pdf}}</description>
<link>https://academictorrents.com/download/ffd9193872a467ea32699c8493efb02ca0f40fdd</link>
</item>
<item>
<title>Euclidean Embedding of Co-occurrence Data (Paper)</title>
<description>@article{8:76,author={Amir Globerson and Gal Chechik and Fernando Pereira and Naftali Tishby}, Title={Euclidean Embedding of Co-occurrence Data},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/globerson07a/globerson07a.pdf}}</description>
<link>https://academictorrents.com/download/5fcf3e50164ab453a28f487f7e8d5f44ddbe1103</link>
</item>
<item>
<title>Learning When Concepts Abound (Paper)</title>
<description>@article{10:89,author={Omid Madani and Michael Connor and Wiley Greiner}, Title={Learning When Concepts Abound},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/madani09a/madani09a.pdf}}</description>
<link>https://academictorrents.com/download/f1e5b72c61f16a4944cb9093db694ae63bf9ce4b</link>
</item>
<item>
<title>A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior (Paper)</title>
<description>@article{6:53,author={Hal Daum III and Daniel Marcu}, Title={A Bayesian Model for Supervised Clustering with the Dirichlet Process Prior},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/daume05a/daume05a.pdf}}</description>
<link>https://academictorrents.com/download/450161730f8fe0c2950c446ea749e4c85c1b2538</link>
</item>
<item>
<title>Learning Module Networks (Paper)</title>
<description>@article{6:19,author={Eran Segal and Dana Pe'er and Aviv Regev and Daphne Koller and Nir Friedman}, Title={Learning Module Networks},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/segal05a/segal05a.pdf}}</description>
<link>https://academictorrents.com/download/238b93637b9599448ee54d04127f17099a8d573f</link>
</item>
<item>
<title>Loop Corrections for Approximate Inference on Factor Graphs (Paper)</title>
<description>@article{8:40,author={Joris M. Mooij and Hilbert J. Kappen}, Title={Loop Corrections for Approximate Inference on Factor Graphs},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/mooij07a/mooij07a.pdf}}</description>
<link>https://academictorrents.com/download/b68dfac9d5f26fb7f89bc0915f5b162bc97a706a</link>
</item>
<item>
<title>Penalized Model-Based Clustering with Application to Variable Selection (Paper)</title>
<description>@article{8:41,author={Wei Pan and Xiaotong Shen}, Title={Penalized Model-Based Clustering with Application to Variable Selection},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/pan07a/pan07a.pdf}}</description>
<link>https://academictorrents.com/download/2e24eccfdef28d63b32ac448840980f79436776b</link>
</item>
<item>
<title>Tree-Based Batch Mode Reinforcement Learning (Paper)</title>
<description>@article{6:18,author={Damien  Ernst and Pierre  Geurts and Louis  Wehenkel}, Title={Tree-Based Batch Mode Reinforcement Learning},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/ernst05a/ernst05a.pdf}}</description>
<link>https://academictorrents.com/download/b81244d60c7d9c0fb203ac3190e8d3582b8f37ba</link>
</item>
<item>
<title>Learning Halfspaces with Malicious Noise (Paper)</title>
<description>@article{10:94,author={Adam R. Klivans and Philip M. Long and Rocco A. Servedio}, Title={Learning Halfspaces with Malicious Noise},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/klivans09a/klivans09a.pdf}}</description>
<link>https://academictorrents.com/download/0bb3abb4a388d52ef2c5b4f1fb86a7785ccce0c2</link>
</item>
<item>
<title>Ranking the Best Instances (Paper)</title>
<description>@article{8:88,author={Stphan Clmenon and Nicolas Vayatis}, Title={Ranking the Best Instances},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/clemencon07a/clemencon07a.pdf}}</description>
<link>https://academictorrents.com/download/06fed548056eed0d17d12d817dd1e57284da2fe8</link>
</item>
<item>
<title>Variational Message Passing (Paper)</title>
<description>@article{6:23,author={John Winn and Christopher M. Bishop}, Title={Variational Message Passing},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/winn05a/winn05a.pdf}}</description>
<link>https://academictorrents.com/download/30ce6f474e843e7b65480679a86612b57c72d86c</link>
</item>
<item>
<title>Managing Diversity in Regression Ensembles (Paper)</title>
<description>@article{6:55,author={Gavin Brown and Jeremy L. Wyatt and Peter Tio}, Title={Managing Diversity in Regression Ensembles},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/brown05a/brown05a.pdf}}</description>
<link>https://academictorrents.com/download/b6457f6bfce562a8be2919e559d5c11c6acfb75c</link>
</item>
<item>
<title>Efficient Online and Batch Learning Using Forward Backward Splitting (Paper)</title>
<description>@article{10:99,author={John Duchi and Yoram Singer}, Title={Efficient Online and Batch Learning Using Forward Backward Splitting},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/duchi09a/duchi09a.pdf}}</description>
<link>https://academictorrents.com/download/73f8e9a0eac100f8ab6409d510f50967c192af34</link>
</item>
<item>
<title>Harnessing the Expertise of 70,000 Human Editors: Knowledge-Based Feature Generation for Text Categorization (Paper)</title>
<description>@article{8:77,author={Evgeniy Gabrilovich and Shaul Markovitch}, Title={Harnessing the Expertise of 70,000 Human Editors: Knowledge-Based Feature Generation for Text Categorization},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/gabrilovich07a/gabrilovich07a.pdf}}</description>
<link>https://academictorrents.com/download/077b92ed26aeecc050c4f268c066563c62e405ce</link>
</item>
<item>
<title>Rearrangement Clustering: Pitfalls, Remedies, and Applications (Paper)</title>
<description>@article{7:32,author={Sharlee Climer and Weixiong Zhang}, Title={Rearrangement Clustering: Pitfalls, Remedies, and Applications},journal={Journal of Machine Learning Research},volume={7}, url={http://www.jmlr.org/papers/volume7/climer06a/climer06a.pdf}}</description>
<link>https://academictorrents.com/download/5e336edd78c873fae1cf334f18fd531a3be8b039</link>
</item>
<item>
<title>Generalization Bounds for Ranking Algorithms via Algorithmic Stability (Paper)</title>
<description>@article{10:16,author={Shivani Agarwal and Partha Niyogi}, Title={Generalization Bounds for Ranking Algorithms via Algorithmic Stability},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/agarwal09a/agarwal09a.pdf}}</description>
<link>https://academictorrents.com/download/aee818944bd83cd8d3ce374cb5bf9c44f1d21019</link>
</item>
<item>
<title>Multiclass Classification with Multi-Prototype Support Vector Machines (Paper)</title>
<description>@article{6:28,author={Fabio Aiolli and Alessandro Sperduti}, Title={Multiclass Classification with Multi-Prototype Support Vector Machines},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/aiolli05a/aiolli05a.pdf}}</description>
<link>https://academictorrents.com/download/96dd2a85db288e33bd9369d0a5b255c0981ba22e</link>
</item>
<item>
<title>Transfer Learning for Reinforcement Learning Domains: A Survey (Paper)</title>
<description>@article{10:56,author={Matthew E. Taylor and Peter Stone}, Title={Transfer Learning for Reinforcement Learning Domains: A Survey},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf}}</description>
<link>https://academictorrents.com/download/062f6d7baad93e2bb253a751e4e13f5ee4310fbb</link>
</item>
<item>
<title>Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners (Paper)</title>
<description>@article{8:86,author={Marlon Nez and Ral Fidalgo and Rafael Morales}, Title={Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/nunez07a/nunez07a.pdf}}</description>
<link>https://academictorrents.com/download/03d75738d3c2a48e92843292acd94aebb60b6711</link>
</item>
<item>
<title>On the Nystrm Method for Approximating a Gram Matrix for Improved Kernel-Based Learning (Paper)</title>
<description>@article{6:72,author={Petros Drineas and Michael W. Mahoney}, Title={On the Nystrm Method for Approximating a Gram Matrix for Improved Kernel-Based Learning},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/drineas05a/drineas05a.pdf}}</description>
<link>https://academictorrents.com/download/b3e686c5b8f75085df7dde5f07d03bdcaa720264</link>
</item>
<item>
<title>Nonextensive Information Theoretic Kernels on Measures (Paper)</title>
<description>@article{10:35,author={Andr F. T. Martins and Noah A. Smith and Eric P. Xing and Pedro M. Q. Aguiar and Mrio A. T. Figueiredo}, Title={Nonextensive Information Theoretic Kernels on Measures},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/martins09a/martins09a.pdf}}</description>
<link>https://academictorrents.com/download/030a9f9b7403eb989205271685a12474c2ba5b26</link>
</item>
<item>
<title>Learning Hidden Variable Networks: The Information Bottleneck Approach (Paper)</title>
<description>@article{6:4,author={Gal Elidan and Nir Friedman}, Title={Learning Hidden Variable Networks: The Information Bottleneck Approach},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/elidan05a/elidan05a.pdf}}</description>
<link>https://academictorrents.com/download/f96a575be7a1439c66f0a5d5b06442275493f346</link>
</item>
<item>
<title>Consistent Feature Selection for Pattern Recognition in Polynomial Time (Paper)</title>
<description>@article{8:21,author={Roland Nilsson and Jos M. Pea and Johan Bjrkegren and Jesper Tegnr}, Title={Consistent Feature Selection for Pattern Recognition in Polynomial Time},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/nilsson07a/nilsson07a.pdf}}</description>
<link>https://academictorrents.com/download/d8c183b2d6c62e5ab43b980a9cec6be2827b5470</link>
</item>
<item>
<title>Refinement of Reproducing Kernels (Paper)</title>
<description>@article{10:4,author={Yuesheng Xu and Haizhang Zhang}, Title={Refinement of Reproducing Kernels},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/xu09a/xu09a.pdf}}</description>
<link>https://academictorrents.com/download/e754ca814cb6c74b06f4c3e409247f88d077aab4</link>
</item>
<item>
<title>Assessing Approximate Inference for Binary Gaussian Process Classification (Paper)</title>
<description>@article{6:57,author={Malte Kuss and Carl Edward Rasmussen}, Title={Assessing Approximate Inference for Binary Gaussian Process Classification},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/kuss05a/kuss05a.pdf}}</description>
<link>https://academictorrents.com/download/7c786a0cd4404f1266ef86e6a3523155755b0064</link>
</item>
<item>
<title>Noise Tolerant Variants of the Perceptron Algorithm (Paper)</title>
<description>@article{8:8,author={Roni Khardon and Gabriel Wachman}, Title={Noise Tolerant Variants of the Perceptron Algorithm},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/khardon07a/khardon07a.pdf}}</description>
<link>https://academictorrents.com/download/15ce9dd60de4798bab0b6a20d538bf14d05bcf76</link>
</item>
<item>
<title>Smooth -Insensitive Regression by Loss Symmetrization (Paper)</title>
<description>@article{6:25,author={Ofer Dekel and Shai Shalev-Shwartz and Yoram Singer}, Title={Smooth -Insensitive Regression by Loss Symmetrization},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/dekel05a/dekel05a.pdf}}</description>
<link>https://academictorrents.com/download/f7a3961beba097d7af71accdb1da74a7eb64095e</link>
</item>
<item>
<title>Belief Propagation for Continuous State Spaces: Stochastic Message-Passing with Quantitative Guarantees (Paper)</title>
<description>@article{14:86,author={Nima Noorshams and Martin J. Wainwright}, Title={Belief Propagation for Continuous State Spaces: Stochastic Message-Passing with Quantitative Guarantees},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/noorshams13a/noorshams13a.pdf}}</description>
<link>https://academictorrents.com/download/deffa88321d73009aa74fd5f5d958adf147e92cd</link>
</item>
<item>
<title>Cautious Collective Classification (Paper)</title>
<description>@article{10:96,author={Luke K. McDowell and Kalyan Moy Gupta and David W. Aha}, Title={Cautious Collective Classification},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/mcdowell09a/mcdowell09a.pdf}}</description>
<link>https://academictorrents.com/download/8a59a0bcd00c453594f7b284cf929bc1fbf63f5c</link>
</item>
<item>
<title>Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data (Paper)</title>
<description>@article{8:84,author={Zakria Hussain and Franois Laviolette and Mario Marchand and John Shawe-Taylor and Spencer Charles Brubaker and Matthew D. Mullin}, Title={Revised Loss Bounds for the Set Covering Machine and Sample-Compression Loss Bounds for Imbalanced Data},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/hussain07a/hussain07a.pdf}}</description>
<link>https://academictorrents.com/download/26490a33e07013d27b8cb1b0ff2cf6b899d82b5c</link>
</item>
<item>
<title>Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets" (Paper)</title>
<description>@article{8:11,author={Galle Loosli and Stphane Canu}, Title={Comments on the "Core Vector Machines: Fast SVM Training on Very Large Data Sets"},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/loosli07a/loosli07a.pdf}}</description>
<link>https://academictorrents.com/download/62e9371183b2b5d0df2bf79113fd91740d9dcbfd</link>
</item>
<item>
<title>An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs (Paper)</title>
<description>@article{10:3,author={M. Pawan Kumar and Vladimir Kolmogorov and Philip H.S. Torr}, Title={An Analysis of Convex Relaxations for MAP Estimation of Discrete MRFs},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/kumar09a/kumar09a.pdf}}</description>
<link>https://academictorrents.com/download/1ec2b027687067e295a2b1a03d611cf4d0c2f37e</link>
</item>
<item>
<title>Gaussian Processes for Ordinal Regression (Paper)</title>
<description>@article{6:35,author={Wei Chu and Zoubin Ghahramani}, Title={Gaussian Processes for Ordinal Regression},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/chu05a/chu05a.pdf}}</description>
<link>https://academictorrents.com/download/74e4922391a20e797c0e38949968f0329d7e396c</link>
</item>
<item>
<title>Large Margin Methods for Structured and Interdependent Output Variables (Paper)</title>
<description>@article{6:50,author={Ioannis Tsochantaridis and Thorsten Joachims and Thomas Hofmann and Yasemin Altun}, Title={Large Margin Methods for Structured and Interdependent Output Variables},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/tsochantaridis05a/tsochantaridis05a.pdf}}</description>
<link>https://academictorrents.com/download/993fe72cb0a9a730e0ae5ec863f4a346142399e2</link>
</item>
<item>
<title>Stability of Randomized Learning Algorithms (Paper)</title>
<description>@article{6:3,author={Andre Elisseeff and Theodoros Evgeniou and Massimiliano Pontil}, Title={Stability of Randomized Learning Algorithms},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/elisseeff05a/elisseeff05a.pdf}}</description>
<link>https://academictorrents.com/download/18cf38dccb2548a5213233c1c11e3ae4438d4ca3</link>
</item>
<item>
<title>Learning from Examples as an Inverse Problem (Paper)</title>
<description>@article{6:30,author={Ernesto De Vito and Lorenzo Rosasco and Andrea Caponnetto and Umberto De Giovannini and Francesca Odone}, Title={Learning from Examples as an Inverse Problem},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/devito05a/devito05a.pdf}}</description>
<link>https://academictorrents.com/download/18d7de9c446474fba3abd5fa46f050d3616cc5e1</link>
</item>
<item>
<title>A Unifying View of Sparse Approximate Gaussian Process Regression (Paper)</title>
<description>@article{6:65,author={Joaquin Quionero-Candela and Carl Edward Rasmussen}, Title={A Unifying View of Sparse Approximate Gaussian Process Regression},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/quinonero-candela05a/quinonero-candela05a.pdf}}</description>
<link>https://academictorrents.com/download/963e38f4120b760c06855adc29cada57bc9c3086</link>
</item>
<item>
<title>Classification with Gaussians and Convex Loss (Paper)</title>
<description>@article{10:49,author={Dao-Hong Xiang and Ding-Xuan Zhou}, Title={Classification with Gaussians and Convex Loss},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/xiang09a/xiang09a.pdf}}</description>
<link>https://academictorrents.com/download/fa6ea4ede21e0185c73f7f6e352c68fb6874a4a7</link>
</item>
<item>
<title>Prioritization Methods for Accelerating MDP Solvers (Paper)</title>
<description>@article{6:29,author={David Wingate and Kevin D. Seppi}, Title={Prioritization Methods for Accelerating MDP Solvers},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/wingate05a/wingate05a.pdf}}</description>
<link>https://academictorrents.com/download/513be53da526050b2f4fbcded68e6fe284c3fcec</link>
</item>
<item>
<title>Classification in Networked Data: A Toolkit and a Univariate Case Study (Paper)</title>
<description>@article{8:34,author={Sofus A. Macskassy and Foster Provost}, Title={Classification in Networked Data: A Toolkit and a Univariate Case Study},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/macskassy07a/macskassy07a.pdf}}</description>
<link>https://academictorrents.com/download/c59e123ab69fe2b2002f3af9a2ba3c02963293ea</link>
</item>
<item>
<title>Hash Kernels for Structured Data (Paper)</title>
<description>@article{10:90,author={Qinfeng Shi and James Petterson and Gideon Dror and John Langford and Alex Smola and S.V.N. Vishwanathan}, Title={Hash Kernels for Structured Data},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/shi09a/shi09a.pdf}}</description>
<link>https://academictorrents.com/download/faf29bb0ba94e087f1a8ad8dfdfe51a7f078e207</link>
</item>
<item>
<title>On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning (Paper)</title>
<description>@article{8:53,author={Rie Johnson and Tong Zhang}, Title={On the Effectiveness of Laplacian Normalization for Graph Semi-supervised Learning},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/johnson07a/johnson07a.pdf}}</description>
<link>https://academictorrents.com/download/30d522121723e2032764a9a2db95108fda7ed637</link>
</item>
<item>
<title>Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling (Paper)</title>
<description>@article{14:84,author={Shusen Wang and Zhihua Zhang}, Title={Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/wang13c/wang13c.pdf}}</description>
<link>https://academictorrents.com/download/c9e45bd1ca311546e2695e614da5ba3f07b9c24f</link>
</item>
<item>
<title>Learning Multiple Tasks with Kernel Methods (Paper)</title>
<description>@article{6:21,author={Theodoros Evgeniou and Charles A. Micchelli and Massimiliano Pontil}, Title={Learning Multiple Tasks with Kernel Methods},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/evgeniou05a/evgeniou05a.pdf}}</description>
<link>https://academictorrents.com/download/6e30834fce44ef979e8c369ce942760284c550be</link>
</item>
<item>
<title>Consistency and Localizability (Paper)</title>
<description>@article{10:30,author={Alon Zakai and Ya'acov Ritov}, Title={Consistency and Localizability},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/zakai09a/zakai09a.pdf}}</description>
<link>https://academictorrents.com/download/c4d2545b047bdf64371c87bc26833b977c1fe8d8</link>
</item>
<item>
<title>RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments(Machine Learning Open Source Software Paper) (Paper)</title>
<description>@article{10:74,author={Brian Tanner and Adam White}, Title={RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments(Machine Learning Open Source Software Paper)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/tanner09a/tanner09a.pdf}}</description>
<link>https://academictorrents.com/download/c08bf14da29f4bf06dd18a4d530910390181e330</link>
</item>
<item>
<title>Multiclass Boosting for Weak Classifiers (Paper)</title>
<description>@article{6:7,author={Gnther Eibl and Karl-Peter Pfeiffer}, Title={Multiclass Boosting for Weak Classifiers},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/eibl05a/eibl05a.pdf}}</description>
<link>https://academictorrents.com/download/a92a8bf349ba69781681b3624b2653bdc5ec4398</link>
</item>
<item>
<title>Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification (Paper)</title>
<description>@article{10:57,author={Eitan Greenshtein and Junyong Park}, Title={Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/greenshtein09a/greenshtein09a.pdf}}</description>
<link>https://academictorrents.com/download/9e968a69072c4814b9e3bc4736619f0713d38c67</link>
</item>
<item>
<title>When Is There a Representer Theorem? Vector Versus Matrix Regularizers (Paper)</title>
<description>@article{10:87,author={Andreas Argyriou and Charles A. Micchelli and Massimiliano Pontil}, Title={When Is There a Representer Theorem?  Vector Versus Matrix Regularizers},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/argyriou09a/argyriou09a.pdf}}</description>
<link>https://academictorrents.com/download/27b9e9808ffb19e126acc7ce1746d81fd81030f3</link>
</item>
<item>
<title>Active Learning to Recognize Multiple Types of Plankton (Paper)</title>
<description>@article{6:20,author={Tong Luo and Kurt Kramer and Dmitry B. Goldgof and Lawrence O. Hall and Scott Samson and Andrew Remsen and Thomas Hopkins}, Title={Active Learning to Recognize Multiple Types of Plankton},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/luo05a/luo05a.pdf}}</description>
<link>https://academictorrents.com/download/7d532a9068cf1009a49d40ff7595b57703bbf08e</link>
</item>
<item>
<title>Learning the Kernel with Hyperkernels (Kernel Machines Section) (Paper)</title>
<description>@article{6:36,author={Cheng Soon Ong and Alexander J. Smola and Robert C. Williamson}, Title={Learning the Kernel with Hyperkernels (Kernel Machines Section)},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/ong05a/ong05a.pdf}}</description>
<link>https://academictorrents.com/download/4ab608c98f874ab10ec1b77af3b5ca83c85d2a51</link>
</item>
<item>
<title>Using Local Dependencies within Batches to Improve Large Margin Classifiers (Paper)</title>
<description>@article{10:8,author={Volkan Vural and Glenn Fung and Balaji Krishnapuram and Jennifer G. Dy and Bharat Rao}, Title={Using Local Dependencies within Batches to Improve Large Margin Classifiers},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/vural09a/vural09a.pdf}}</description>
<link>https://academictorrents.com/download/754104894f25f92ea726ac29a6e34d460bd61f5f</link>
</item>
<item>
<title>Information Bottleneck for Gaussian Variables (Paper)</title>
<description>@article{6:6,author={Gal Chechik and Amir Globerson and Naftali Tishby and Yair Weiss}, Title={Information Bottleneck for Gaussian Variables},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/chechik05a/chechik05a.pdf}}</description>
<link>https://academictorrents.com/download/d3cecd5b9df1147c2f62c43bcfb4f1d66f213a9f</link>
</item>
<item>
<title>Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application (Paper)</title>
<description>@article{6:27,author={Joseph F. Murray and Gordon F. Hughes and Kenneth Kreutz-Delgado}, Title={Machine Learning Methods for Predicting Failures in Hard Drives:  A Multiple-Instance Application},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/murray05a/murray05a.pdf}}</description>
<link>https://academictorrents.com/download/fd1725daad46d01243c91e36fa9226ef6f1e374c</link>
</item>
<item>
<title>What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks (Paper)</title>
<description>@article{6:66,author={Weng-Keen Wong and Andrew Moore and Gregory Cooper and Michael Wagner}, Title={What's Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/wong05a/wong05a.pdf}}</description>
<link>https://academictorrents.com/download/5a52cfe03369a59117da5ee5c9660c1c8e1a19f1</link>
</item>
<item>
<title>A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data (Paper)</title>
<description>@article{6:61,author={Rie Kubota Ando and Tong Zhang}, Title={A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/ando05a/ando05a.pdf}}</description>
<link>https://academictorrents.com/download/f4470eb8bc3a6f697df61bde319fd56e3a9d6733</link>
</item>
<item>
<title>An MDP-Based Recommender System (Paper)</title>
<description>@article{6:43,author={Guy Shani and David Heckerman and Ronen I. Brafman}, Title={An MDP-Based Recommender System},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/shani05a/shani05a.pdf}}</description>
<link>https://academictorrents.com/download/c081d6f2e2177694c10aa6d77b0f569d718aa277</link>
</item>
<item>
<title>Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods (Paper)</title>
<description>@article{10:32,author={Holger Hfling and Robert Tibshirani}, Title={Estimation of Sparse Binary Pairwise Markov Networks using Pseudo-likelihoods},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/hoefling09a/hoefling09a.pdf}}</description>
<link>https://academictorrents.com/download/821b0dafee4af63753f358d0e28ade1c0dbaba07</link>
</item>
<item>
<title>Nonlinear Boosting Projections for Ensemble Construction (Paper)</title>
<description>@article{8:1,author={Nicols Garca-Pedrajas and Csar Garca-Osorio and Colin Fyfe}, Title={Nonlinear Boosting Projections for Ensemble Construction},journal={Journal of Machine Learning Research},volume={8}, url={http://www.jmlr.org/papers/volume8/garcia-pedrajas07a/garcia-pedrajas07a.pdf}}</description>
<link>https://academictorrents.com/download/26f0ee842a3ac66f7e1a75d9585a06872284150b</link>
</item>
<item>
<title>Scalable Collaborative Filtering Approaches for Large Recommender Systems(Special Topic on Mining and Learning with Graphs and Relations) (Paper)</title>
<description>@article{10:22,author={Gbor Takcs and Istvn Pilszy and Bottyn Nmeth and Domonkos Tikk}, Title={Scalable Collaborative Filtering Approaches for Large Recommender Systems(Special Topic on Mining and Learning with Graphs and Relations)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/takacs09a/takacs09a.pdf}}</description>
<link>https://academictorrents.com/download/c9a25c873abe71f40c1fc3719f476fe1d4386c76</link>
</item>
<item>
<title>GPstuff: Bayesian Modeling with Gaussian Processes (Paper)</title>
<description>@article{14:36,author={Jarno Vanhatalo and Jaakko Riihimki and Jouni Hartikainen and Pasi Jylnki and Ville Tolvanen and Aki Vehtari}, Title={GPstuff: Bayesian Modeling with Gaussian Processes},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/vanhatalo13a/vanhatalo13a.pdf}}</description>
<link>https://academictorrents.com/download/5c019c7cab9d0872cb5bcdd8d0ce2e44e8c12ef6</link>
</item>
<item>
<title>Beyond Fano's Inequality: Bounds on the Optimal F-Score, BER, and Cost-Sensitive Risk and Their Implications (Paper)</title>
<description>@article{14:33,author={Ming-Jie Zhao and Narayanan Edakunni and Adam Pocock and Gavin Brown}, Title={Beyond Fano's Inequality: Bounds on the Optimal F-Score, BER, and Cost-Sensitive Risk and Their Implications},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/zhao13a/zhao13a.pdf}}</description>
<link>https://academictorrents.com/download/3f4351f654434c014ffa7bd11b3bec535848fc15</link>
</item>
<item>
<title>Java-ML: A Machine Learning Library(Machine Learning Open Source Software Paper) (Paper)</title>
<description>@article{10:34,author={Thomas Abeel and Yves Van de Peer and Yvan Saeys}, Title={Java-ML: A Machine Learning Library(Machine Learning Open Source Software Paper)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/abeel09a/abeel09a.pdf}}</description>
<link>https://academictorrents.com/download/51f1fcb31e6a52e3ef2e30d60a9c7c03b82d8b3f</link>
</item>
<item>
<title>Ultrahigh Dimensional Feature Selection: Beyond The Linear Model (Paper)</title>
<description>@article{10:70,author={Jianqing Fan and Richard Samworth and Yichao Wu}, Title={Ultrahigh Dimensional Feature Selection: Beyond The Linear Model},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/fan09a/fan09a.pdf}}</description>
<link>https://academictorrents.com/download/58d5bf59fec3abe12ec451279938a4bc71ad6bfa</link>
</item>
<item>
<title>MAGIC Summoning: Towards Automatic Suggesting and Testing of Gestures With Low Probability of False Positives During Use (Paper)</title>
<description>@article{14:7,author={Daniel Kyu Hwa Kohlsdorf and Thad E. Starner}, Title={MAGIC Summoning:  Towards Automatic Suggesting and Testing of Gestures With Low Probability of False Positives During Use},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/kohlsdorf13a/kohlsdorf13a.pdf}}</description>
<link>https://academictorrents.com/download/19f00a21ce64d05278c5ecee12b6d0c1c2bd4b84</link>
</item>
<item>
<title>An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity (Paper)</title>
<description>@article{10:38,author={Jose M. Pea and Roland Nilsson and Johan Bjrkegren and Jesper Tegnr}, Title={An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/pena09a/pena09a.pdf}}</description>
<link>https://academictorrents.com/download/519e15dbeab4f1c9fb79c7699f75db6089c6b6bf</link>
</item>
<item>
<title>Denoising Source Separation (Paper)</title>
<description>@article{6:9,author={Jaakko Srel and Harri Valpola}, Title={Denoising Source Separation},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/sarela05a/sarela05a.pdf}}</description>
<link>https://academictorrents.com/download/510729a50981a497de66ac7cec570eb08a6b7c79</link>
</item>
<item>
<title>Expectation Consistent Approximate Inference (Paper)</title>
<description>@article{6:73,author={Manfred Opper and Ole Winther}, Title={Expectation Consistent Approximate Inference},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/opper05a/opper05a.pdf}}</description>
<link>https://academictorrents.com/download/3c48b34e2cfd803c83257a6477ac0ab0ef49fa12</link>
</item>
<item>
<title>Sparse Online Learning via Truncated Gradient (Paper)</title>
<description>@article{10:28,author={John Langford and Lihong Li and Tong Zhang}, Title={Sparse Online Learning via Truncated Gradient},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/langford09a/langford09a.pdf}}</description>
<link>https://academictorrents.com/download/4dc4963adefc2e475287cf84eafe511d01a35d2b</link>
</item>
<item>
<title>Working Set Selection Using Second Order Information for Training Support Vector Machines (Paper)</title>
<description>@article{6:63,author={Rong-En Fan and Pai-Hsuen Chen and Chih-Jen Lin}, Title={Working Set Selection Using Second Order Information for Training Support Vector Machines},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/fan05a/fan05a.pdf}}</description>
<link>https://academictorrents.com/download/8423422eb10dd6a9fbe8a6e1fdb6380913d6bd84</link>
</item>
<item>
<title>Controlling the False Discovery Rate of the AssociationCausality Structure Learned with the PC Algorithm(Special Topic on Mining and Learning with Graphs and Relations) (Paper)</title>
<description>@article{10:17,author={Junning Li and Z. Jane Wang}, Title={Controlling the False Discovery Rate of the AssociationCausality Structure Learned with the PC Algorithm(Special Topic on Mining and Learning with Graphs and Relations)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/li09a/li09a.pdf}}</description>
<link>https://academictorrents.com/download/c621ab96861aed981ed53a1bb39bdd86c6d95467</link>
</item>
<item>
<title>Frames, Reproducing Kernels, Regularization and Learning (Paper)</title>
<description>@article{6:51,author={Alain Rakotomamonjy and  Stphane Canu}, Title={Frames, Reproducing Kernels, Regularization and Learning},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/rakotomamonjy05a/rakotomamonjy05a.pdf}}</description>
<link>https://academictorrents.com/download/f33ce848cd9ff297cf94dee7a196b7b3a6141254</link>
</item>
<item>
<title>Tapkee: An Efficient Dimension Reduction Library (Paper)</title>
<description>@article{14:73,author={Sergey Lisitsyn and Christian Widmer and Fernando J. Iglesias Garcia}, Title={Tapkee: An Efficient Dimension Reduction Library},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/lisitsyn13a/lisitsyn13a.pdf}}</description>
<link>https://academictorrents.com/download/ec09477ac7af2e3bb135819d83619f1a4fcc0c37</link>
</item>
<item>
<title>Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising (Paper)</title>
<description>@article{14:102,author={Lon Bottou and Jonas Peters and Joaquin Quionero-Candela and Denis X. Charles and D. Max Chickering and Elon Portugaly and Dipankar Ray and Patrice Simard and Ed Snelson}, Title={Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/bottou13a/bottou13a.pdf}}</description>
<link>https://academictorrents.com/download/76b9a4b27cd4f3436b349e438be1c1663abeebea</link>
</item>
<item>
<title>Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data(Machine Learning Open Source Software Paper) (Paper)</title>
<description>@article{10:6,author={Abhik Shah and Peter Woolf}, Title={Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data(Machine Learning Open Source Software Paper)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/shah09a/shah09a.pdf}}</description>
<link>https://academictorrents.com/download/e684c0edea6d7ec83fb16980bdcb7e502adef004</link>
</item>
<item>
<title>Variational Algorithms for Marginal MAP (Paper)</title>
<description>@article{14:100,author={Qiang Liu and Alexander Ihler}, Title={Variational Algorithms for Marginal MAP},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/liu13b/liu13b.pdf}}</description>
<link>https://academictorrents.com/download/ef3fd853ccf3a0c8331ab9908d3e4befabf5ef80</link>
</item>
<item>
<title>Distributed Algorithms for Topic Models (Paper)</title>
<description>@article{10:62,author={David Newman and Arthur Asuncion and Padhraic Smyth and Max Welling}, Title={Distributed Algorithms for Topic Models},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/newman09a/newman09a.pdf}}</description>
<link>https://academictorrents.com/download/6a4b9d2eecc190f85a80adffe68ba52da77af94e</link>
</item>
<item>
<title>Parallel Vector Field Embedding (Paper)</title>
<description>@article{14:91,author={Binbin Lin and Xiaofei He and Chiyuan Zhang and Ming Ji}, Title={Parallel Vector Field Embedding},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/lin13a/lin13a.pdf}}</description>
<link>https://academictorrents.com/download/e4a5f0b14499f2660ccfeb92ab3af65557fc2067</link>
</item>
<item>
<title>Multi-task Reinforcement Learning in Partially Observable Stochastic Environments (Paper)</title>
<description>@article{10:40,author={Hui Li and Xuejun Liao and Lawrence Carin}, Title={Multi-task Reinforcement Learning in Partially Observable Stochastic Environments},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/li09b/li09b.pdf}}</description>
<link>https://academictorrents.com/download/902bb50e342e15cafa431ff8c09f2df85b168c0f</link>
</item>
<item>
<title>How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis (Paper)</title>
<description>@article{14:117,author={Alberto N. Escalante-B. and Laurenz Wiskott}, Title={How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/escalante13a/escalante13a.pdf}}</description>
<link>https://academictorrents.com/download/6588c6a4ac6f3af9467374146a433154c82a6ba1</link>
</item>
<item>
<title>Universal Kernel-Based Learning with Applications to Regular Languages(Special Topic on Mining and Learning with Graphs and Relations) (Paper)</title>
<description>@article{10:39,author={Leonid (Aryeh) Kontorovich and Boaz Nadler}, Title={Universal Kernel-Based Learning with Applications to Regular Languages(Special Topic on Mining and Learning with Graphs and Relations)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/kontorovich09a/kontorovich09a.pdf}}</description>
<link>https://academictorrents.com/download/f7268a3b3b5c240236c163bda5d0bbcd6388a94a</link>
</item>
<item>
<title>Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques (Paper)</title>
<description>@article{10:18,author={Barnabs Pczos and Andrs Lorincz}, Title={Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/poczos09a/poczos09a.pdf}}</description>
<link>https://academictorrents.com/download/f620441db22049530cc97763ccc9bddd5769d61b</link>
</item>
<item>
<title>Low-Rank Kernel Learning with Bregman Matrix Divergences (Paper)</title>
<description>@article{10:13,author={Brian Kulis and Mtys A. Sustik and Inderjit S. Dhillon}, Title={Low-Rank Kernel Learning with Bregman Matrix Divergences},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/kulis09a/kulis09a.pdf}}</description>
<link>https://academictorrents.com/download/36fbeaf6bdd3f6743e3b9701d7dc4761275c4f96</link>
</item>
<item>
<title>Learning Acyclic Probabilistic Circuits Using Test Paths (Paper)</title>
<description>@article{10:65,author={Dana Angluin and James Aspnes and Jiang Chen and David Eisenstat and Lev Reyzin}, Title={Learning Acyclic Probabilistic Circuits Using Test Paths},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/angluin09a/angluin09a.pdf}}</description>
<link>https://academictorrents.com/download/7791936dbe592270bf422723b26427053790de17</link>
</item>
<item>
<title>Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning (Paper)</title>
<description>@article{10:61,author={Halbert White and Karim Chalak}, Title={Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/white09a/white09a.pdf}}</description>
<link>https://academictorrents.com/download/214a7984eddbaf4f80683d2d3a25f7f937dfa209</link>
</item>
<item>
<title>Learning Approximate Sequential Patterns for Classification (Paper)</title>
<description>@article{10:66,author={Zeeshan Syed and Piotr Indyk and John Guttag}, Title={Learning Approximate Sequential Patterns for Classification},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/syed09a/syed09a.pdf}}</description>
<link>https://academictorrents.com/download/c0ddd60f3b29fb6c6dad16bfe87b2c4699d42c1b</link>
</item>
<item>
<title>Combining Information Extraction Systems Using Voting and Stacked Generalization (Paper)</title>
<description>@article{6:59,author={Georgios Sigletos and Georgios Paliouras and Constantine D. Spyropoulos and Michalis Hatzopoulos}, Title={Combining Information Extraction Systems Using Voting and Stacked Generalization},journal={Journal of Machine Learning Research},volume={6}, url={http://www.jmlr.org/papers/volume6/sigletos05a/sigletos05a.pdf}}</description>
<link>https://academictorrents.com/download/7f914e88ed18d63b49f0cf8cf4878cb6d2b7b348</link>
</item>
<item>
<title>Adaptive False Discovery Rate Control under Independence and Dependence (Paper)</title>
<description>@article{10:97,author={Gilles Blanchard and  tienne Roquain}, Title={Adaptive False Discovery Rate Control under Independence and Dependence},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/blanchard09a/blanchard09a.pdf}}</description>
<link>https://academictorrents.com/download/a6505f1745d9214d0fbc5bf66f7b790d8e8cc363</link>
</item>
<item>
<title>Learning Linear Ranking Functions for Beam Search with Application to Planning (Paper)</title>
<description>@article{10:54,author={Yuehua Xu and Alan Fern and Sungwook Yoon}, Title={Learning Linear Ranking Functions for Beam Search with Application to Planning},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/xu09c/xu09c.pdf}}</description>
<link>https://academictorrents.com/download/8c2e77b844696e7c0960ea4dd415192de1afc31e</link>
</item>
<item>
<title>SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent (Paper)</title>
<description>@article{10:59,author={Antoine Bordes and Lon Bottou and Patrick Gallinari}, Title={SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/bordes09a/bordes09a.pdf}}</description>
<link>https://academictorrents.com/download/b34015056bd14ce7c9ef67cd8f4e209a8c7dc697</link>
</item>
<item>
<title>Bayesian Network Structure Learning by Recursive Autonomy Identification (Paper)</title>
<description>@article{10:53,author={Raanan Yehezkel and Boaz Lerner}, Title={Bayesian Network Structure Learning by Recursive Autonomy Identification},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/yehezkel09a/yehezkel09a.pdf}}</description>
<link>https://academictorrents.com/download/148196cd681145324b6e1ac5b92572ea388e1827</link>
</item>
<item>
<title>Sub-Local Constraint-Based Learning of Bayesian Networks Using A Joint Dependence Criterion (Paper)</title>
<description>@article{14:50,author={Rami Mahdi and Jason Mezey}, Title={Sub-Local Constraint-Based Learning of Bayesian Networks Using A Joint Dependence Criterion},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/mahdi13a/mahdi13a.pdf}}</description>
<link>https://academictorrents.com/download/3cc32b00695bb7bb972bbcdf8e75fb666463db86</link>
</item>
<item>
<title>Global Analytic Solution of Fully-observed Variational Bayesian Matrix Factorization (Paper)</title>
<description>@article{14:1,author={Shinichi Nakajima and Masashi Sugiyama and S. Derin Babacan and Ryota Tomioka}, Title={Global Analytic Solution of Fully-observed Variational Bayesian Matrix Factorization},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/nakajima13a/nakajima13a.pdf}}</description>
<link>https://academictorrents.com/download/14361906a005e3523b5470dafe249c2b873cdcca</link>
</item>
<item>
<title>Stable and Efficient Gaussian Process Calculations (Paper)</title>
<description>@article{10:31,author={Leslie Foster and Alex Waagen and Nabeela Aijaz and Michael Hurley and Apolonio Luis and Joel Rinsky and Chandrika Satyavolu and Michael J. Way and Paul Gazis and Ashok Srivastava}, Title={Stable and Efficient Gaussian Process Calculations},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/foster09a/foster09a.pdf}}</description>
<link>https://academictorrents.com/download/e57b2069c47c25a5bb00f886c925a305eceef20d</link>
</item>
<item>
<title>Gaussian Kullback-Leibler Approximate Inference (Paper)</title>
<description>@article{14:69,author={Edward Challis and David Barber}, Title={Gaussian Kullback-Leibler Approximate Inference},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/challis13a/challis13a.pdf}}</description>
<link>https://academictorrents.com/download/92db2e5b8a0e24d94e5234e948049f6bc6cc9438</link>
</item>
<item>
<title>Similarity-based Clustering by Left-Stochastic Matrix Factorization (Paper)</title>
<description>@article{14:54,author={Raman Arora and Maya R. Gupta and Amol Kapila and Maryam Fazel}, Title={Similarity-based Clustering by Left-Stochastic Matrix Factorization},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/arora13a/arora13a.pdf}}</description>
<link>https://academictorrents.com/download/f2dec4ca78971464b95b3abad3505fd03b5c2cc1</link>
</item>
<item>
<title>Hybrid MPIOpenMP Parallel Linear Support Vector Machine Training (Paper)</title>
<description>@article{10:67,author={Kristian Woodsend and Jacek Gondzio}, Title={Hybrid MPIOpenMP Parallel Linear Support Vector Machine Training},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/woodsend09a/woodsend09a.pdf}}</description>
<link>https://academictorrents.com/download/cadd0708c887e5e73fceb9cbec3123f5e6150822</link>
</item>
<item>
<title>Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods (Paper)</title>
<description>@article{10:73,author={Christian Rieger and Barbara Zwicknagl}, Title={Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/rieger09a/rieger09a.pdf}}</description>
<link>https://academictorrents.com/download/1ad9699408b1524336afed9ad9a4d04295f6b691</link>
</item>
<item>
<title>Large-scale SVD and Manifold Learning (Paper)</title>
<description>@article{14:97,author={Ameet Talwalkar and Sanjiv Kumar and Mehryar Mohri and Henry Rowley}, Title={Large-scale SVD and Manifold Learning},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/talwalkar13a/talwalkar13a.pdf}}</description>
<link>https://academictorrents.com/download/57bd784d8ae732ff60e330239ff43ef62c2c3abc</link>
</item>
<item>
<title>Algorithms for Discovery of Multiple Markov Boundaries (Paper)</title>
<description>@article{14:16,author={Alexander Statnikov and Nikita I. Lytkin and Jan Lemeire and Constantin F. Aliferis}, Title={Algorithms for Discovery of Multiple Markov Boundaries},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/statnikov13a/statnikov13a.pdf}}</description>
<link>https://academictorrents.com/download/68408d7bda78720ac26f8e057d8f8cde9abd5718</link>
</item>
<item>
<title>Similarity-based Classification: Concepts and Algorithms (Paper)</title>
<description>@article{10:27,author={Yihua Chen and Eric K. Garcia and Maya R. Gupta and Ali Rahimi and Luca Cazzanti}, Title={Similarity-based Classification: Concepts and Algorithms},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/chen09a/chen09a.pdf}}</description>
<link>https://academictorrents.com/download/bcdd07948277fb3b20b1727ef2104107a05995eb</link>
</item>
<item>
<title>Prediction With Expert Advice For The Brier Game (Paper)</title>
<description>@article{10:85,author={Vladimir Vovk and Fedor Zhdanov}, Title={Prediction With Expert Advice For The Brier Game},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/vovk09a/vovk09a.pdf}}</description>
<link>https://academictorrents.com/download/5839ddb77c57af03dd14e4b9da0cd7ce727664ae</link>
</item>
<item>
<title>The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models (Paper)</title>
<description>@article{10:41,author={Ricardo Silva and Zoubin Ghahramani}, Title={The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/silva09a/silva09a.pdf}}</description>
<link>https://academictorrents.com/download/3f017fc1ceb243bf9c912df7e9a81da265ff1122</link>
</item>
<item>
<title>Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation (Paper)</title>
<description>@article{14:60,author={Daniel Hernndez-Lobato and Jos Miguel Hernndez-Lobato and Pierre Dupont}, Title={Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/hernandez-lobato13a/hernandez-lobato13a.pdf}}</description>
<link>https://academictorrents.com/download/e5b57ee36c72734c8692c50356be672c12d35fed</link>
</item>
<item>
<title>Estimating Labels from Label Proportions (Paper)</title>
<description>@article{10:82,author={Novi Quadrianto and Alex J. Smola and Tibrio S. Caetano and Quoc V. Le}, Title={Estimating Labels from Label Proportions},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/quadrianto09a/quadrianto09a.pdf}}</description>
<link>https://academictorrents.com/download/8dc40264fcbfb8c203cb0fd94fa207d0162c7c53</link>
</item>
<item>
<title>Robust Process Discovery with Artificial Negative Events(Special Topic on Mining and Learning with Graphs and Relations) (Paper)</title>
<description>@article{10:44,author={Stijn Goedertier and David Martens and Jan Vanthienen and Bart Baesens}, Title={Robust Process Discovery with Artificial Negative Events(Special Topic on Mining and Learning with Graphs and Relations)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/goedertier09a/goedertier09a.pdf}}</description>
<link>https://academictorrents.com/download/25d166d65b19493fe44c29af341d71589179b2d3</link>
</item>
<item>
<title>Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection (Paper)</title>
<description>@article{10:69,author={Jie Chen and Haw-ren Fang and Yousef Saad}, Title={Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/chen09b/chen09b.pdf}}</description>
<link>https://academictorrents.com/download/2a95276bf51b951ddafdf9681ff18ecba03b92ea</link>
</item>
<item>
<title>Lovasz theta function, SVMs and Finding Dense Subgraphs (Paper)</title>
<description>@article{14:111,author={Vinay Jethava and Anders Martinsson and Chiranjib Bhattacharyya and Devdatt Dubhashi}, Title={Lovasz theta function, SVMs and Finding Dense Subgraphs},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/jethava13a/jethava13a.pdf}}</description>
<link>https://academictorrents.com/download/67debaedcfd2ce2480e419ce3a44156135d38b8a</link>
</item>
<item>
<title>Perturbation Corrections in Approximate Inference: Mixture Modelling Applications (Paper)</title>
<description>@article{10:43,author={Ulrich Paquet and Ole Winther and Manfred Opper}, Title={Perturbation Corrections in Approximate Inference: Mixture Modelling Applications},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/paquet09a/paquet09a.pdf}}</description>
<link>https://academictorrents.com/download/503dcb099f984a19d76edd8303fa9915f4894edb</link>
</item>
<item>
<title>Stationary-Sparse Causality Network Learning (Paper)</title>
<description>@article{14:95,author={Yuejia He and Yiyuan She and Dapeng Wu}, Title={Stationary-Sparse Causality Network Learning},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/he13a/he13a.pdf}}</description>
<link>https://academictorrents.com/download/7a3ecd1c3f81fb6a0dda78bc1c2ff8d87758d759</link>
</item>
<item>
<title>Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors (Paper)</title>
<description>@article{10:81,author={Mathias Drton and Michael Eichler and Thomas S. Richardson}, Title={Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/drton09a/drton09a.pdf}}</description>
<link>https://academictorrents.com/download/7cb135851a967364a44b500ef1382524390f5fd7</link>
</item>
<item>
<title>Quasi-Newton Method: A New Direction (Paper)</title>
<description>@article{14:27,author={Philipp Hennig and Martin Kiefel}, Title={Quasi-Newton Method: A New Direction},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/hennig13a/hennig13a.pdf}}</description>
<link>https://academictorrents.com/download/56fa80cbecf20bd53066d0cbd4b84d5224236912</link>
</item>
<item>
<title>Data-driven Calibration of Penalties for Least-Squares Regression (Paper)</title>
<description>@article{10:10,author={Sylvain Arlot and Pascal Massart}, Title={Data-driven Calibration of Penalties for Least-Squares Regression},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/arlot09a/arlot09a.pdf}}</description>
<link>https://academictorrents.com/download/fe4da40a6b0027d3ae8c9531cbe9224014e56af1</link>
</item>
<item>
<title>Analysis of Perceptron-Based Active Learning (Paper)</title>
<description>@article{10:11,author={Sanjoy Dasgupta and Adam Tauman Kalai and Claire Monteleoni}, Title={Analysis of Perceptron-Based Active Learning},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/dasgupta09a/dasgupta09a.pdf}}</description>
<link>https://academictorrents.com/download/dcff855a3bbf542f6784ee85764cb96ac13c6e55</link>
</item>
<item>
<title>Properties of Monotonic Effects on Directed Acyclic Graphs (Paper)</title>
<description>@article{10:24,author={Tyler J. VanderWeele and James M. Robins}, Title={Properties of Monotonic Effects on Directed Acyclic Graphs},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/vanderweele09a/vanderweele09a.pdf}}</description>
<link>https://academictorrents.com/download/78411eabf3e1484d3bd97b2b03fe8cbfb3316859</link>
</item>
<item>
<title>CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning (Paper)</title>
<description>@article{10:64,author={Roberto Esposito and Daniele P. Radicioni}, Title={CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/esposito09a/esposito09a.pdf}}</description>
<link>https://academictorrents.com/download/59c4f8d17a1b6bf8cb10135b87dbd2309ef7c188</link>
</item>
<item>
<title>Truncated Power Method for Sparse Eigenvalue Problems (Paper)</title>
<description>@article{14:29,author={Xiao-Tong Yuan and Tong Zhang}, Title={Truncated Power Method for Sparse Eigenvalue Problems},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/yuan13a/yuan13a.pdf}}</description>
<link>https://academictorrents.com/download/807e9ca4e5e7303780944acba61182f8f81717d0</link>
</item>
<item>
<title>Sparse Matrix Inversion with Scaled Lasso (Paper)</title>
<description>@article{14:107,author={Tingni Sun and Cun-Hui Zhang}, Title={Sparse Matrix Inversion with Scaled Lasso},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/sun13a/sun13a.pdf}}</description>
<link>https://academictorrents.com/download/46e403867091a86d6ccc31f9808beedf52adf803</link>
</item>
<item>
<title>Robustness and Regularization of Support Vector Machines (Paper)</title>
<description>@article{10:51,author={Huan Xu and Constantine Caramanis and Shie Mannor}, Title={Robustness and Regularization of Support Vector Machines},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/xu09b/xu09b.pdf}}</description>
<link>https://academictorrents.com/download/c213084c164b0d7f80c6b816ceee199b697e2dc3</link>
</item>
<item>
<title>Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models (Paper)</title>
<description>@article{14:88,author={Manfred Opper and Ulrich Paquet and Ole Winther}, Title={Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/opper13a/opper13a.pdf}}</description>
<link>https://academictorrents.com/download/c039fcb1cac9bff23da0ac663e0d1bee6f4aae9c</link>
</item>
<item>
<title>Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions (Paper)</title>
<description>@article{10:83,author={Lisa Hellerstein and Bernard Rosell and Eric Bach and Soumya Ray and David Page}, Title={Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/hellerstein09a/hellerstein09a.pdf}}</description>
<link>https://academictorrents.com/download/2a0b3a40040592aef9c4c747bd0e4997360d8e3d</link>
</item>
<item>
<title>Keep It Simple And Sparse: Real-Time Action Recognition (Paper)</title>
<description>@article{14:81,author={Sean Ryan Fanello and Ilaria Gori and Giorgio Metta and Francesca Odone}, Title={Keep It Simple And Sparse: Real-Time Action Recognition},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/fanello13a/fanello13a.pdf}}</description>
<link>https://academictorrents.com/download/0216db9a867c799c7da89b3a052c4a6293b4d7c8</link>
</item>
<item>
<title>Online Learning with Sample Path Constraints (Paper)</title>
<description>@article{10:20,author={Shie Mannor and John N. Tsitsiklis and Jia Yuan Yu}, Title={Online Learning with Sample Path Constraints},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/mannor09a/mannor09a.pdf}}</description>
<link>https://academictorrents.com/download/03465825ecd565378cacfa979f041b0d8f2593be</link>
</item>
<item>
<title>Reinforcement Learning in Finite MDPs: PAC Analysis (Paper)</title>
<description>@article{10:84,author={Alexander L. Strehl and Lihong Li and Michael L. Littman}, Title={Reinforcement Learning in Finite MDPs: PAC Analysis},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/strehl09a/strehl09a.pdf}}</description>
<link>https://academictorrents.com/download/2cd4976d263fd6e5d4b1f8e2f22438b1b82e9cde</link>
</item>
<item>
<title>A Survey of Accuracy Evaluation Metrics of Recommendation Tasks (Paper)</title>
<description>@article{10:100,author={Asela Gunawardana and Guy Shani}, Title={A Survey of Accuracy Evaluation Metrics of Recommendation Tasks},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/gunawardana09a/gunawardana09a.pdf}}</description>
<link>https://academictorrents.com/download/b31f8310f96f1b3563bdb7a3d11878711b63b000</link>
</item>
<item>
<title>Orange: Data Mining Toolbox in Python (Paper)</title>
<description>@article{14:72,author={Janez Demar and Toma Curk and Ale Erjavec and rt Gorup and Toma Hoevar and Mitar Milutinovi and Martin Moina and Matija Polajnar and Marko Toplak and Ane Stari and Miha tajdohar and Lan Umek and Lan agar and Jure bontar and Marinka itnik and Bla Zupan}, Title={Orange: Data Mining Toolbox in Python},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/demsar13a/demsar13a.pdf}}</description>
<link>https://academictorrents.com/download/78e7fe1bb1b432f24c4e0226a25d02c2a8ca60c2</link>
</item>
<item>
<title>Multivariate Convex Regression with Adaptive Partitioning (Paper)</title>
<description>@article{14:103,author={Lauren A. Hannah and David B. Dunson}, Title={Multivariate Convex Regression with Adaptive Partitioning},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/hannah13a/hannah13a.pdf}}</description>
<link>https://academictorrents.com/download/0e27179d76982da01c8341a89719f8f227ee9ae4</link>
</item>
<item>
<title>Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination(Special Topic on Model Selection) (Paper)</title>
<description>@article{10:45,author={Eugene Tuv and Alexander Borisov and George Runger and Kari Torkkola}, Title={Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination(Special Topic on Model Selection)},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/tuv09a/tuv09a.pdf}}</description>
<link>https://academictorrents.com/download/a6f1ed86b6f94fa6834470612936ff64da1a8ae9</link>
</item>
<item>
<title>Dynamic Affine-Invariant Shape-Appearance Handshape Features and Classification in Sign Language Videos (Paper)</title>
<description>@article{14:52,author={Anastasios Roussos and Stavros Theodorakis and Vassilis Pitsikalis and Petros Maragos}, Title={Dynamic Affine-Invariant Shape-Appearance Handshape Features and Classification in Sign Language Videos},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/roussos13a/roussos13a.pdf}}</description>
<link>https://academictorrents.com/download/6a0ba63fce87c30813082fc424ed2328836b7bbf</link>
</item>
<item>
<title>Distributions of Angles in Random Packing on Spheres (Paper)</title>
<description>@article{14:58,author={Tony Cai and Jianqing Fan and Tiefeng Jiang}, Title={Distributions of Angles in Random Packing on Spheres},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/cai13a/cai13a.pdf}}</description>
<link>https://academictorrents.com/download/2cef7ef506fad29fe636bc6c67d7dd3045bc011b</link>
</item>
<item>
<title>Alleviating Naive Bayes Attribute Independence Assumption by Attribute Weighting (Paper)</title>
<description>@article{14:61,author={Nayyar A. Zaidi and Jess Cerquides and Mark J. Carman and Geoffrey I. Webb}, Title={Alleviating Naive Bayes Attribute Independence Assumption by Attribute Weighting},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/zaidi13a/zaidi13a.pdf}}</description>
<link>https://academictorrents.com/download/13a66360265771ac37ad4fe29881cebb33a764e7</link>
</item>
<item>
<title>Greedy Sparsity-Constrained Optimization (Paper)</title>
<description>@article{14:26,author={Sohail Bahmani and Bhiksha Raj and Petros T. Boufounos}, Title={Greedy Sparsity-Constrained Optimization},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/bahmani13a/bahmani13a.pdf}}</description>
<link>https://academictorrents.com/download/3586e9c854fd5ee46fee11267c739f656e5cbafc</link>
</item>
<item>
<title>Lower Bounds and Selectivity of Weak-Consistent Policies in Stochastic Multi-Armed Bandit Problem (Paper)</title>
<description>@article{14:6,author={Antoine Salomon and Jean-Yves Audibert and Issam El Alaoui}, Title={Lower Bounds and Selectivity of Weak-Consistent Policies in Stochastic Multi-Armed Bandit Problem},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/salomon13a/salomon13a.pdf}}</description>
<link>https://academictorrents.com/download/9e40b4952217ba1d7e8995563dfa9bb7ed456506</link>
</item>
<item>
<title>The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs (Paper)</title>
<description>@article{10:80,author={Han Liu and John Lafferty and Larry Wasserman}, Title={The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/liu09a/liu09a.pdf}}</description>
<link>https://academictorrents.com/download/0e020decfe281838b7b7509664c72c4fc51c4cb0</link>
</item>
<item>
<title>BudgetedSVM: A Toolbox for Scalable SVM Approximations (Paper)</title>
<description>@article{14:121,author={Nemanja Djuric and Liang Lan and Slobodan Vucetic and Zhuang Wang}, Title={BudgetedSVM: A Toolbox for Scalable SVM Approximations},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/djuric13a/djuric13a.pdf}}</description>
<link>https://academictorrents.com/download/b15610978ec89ae1a633e4f4a9dca94a0e22815a</link>
</item>
<item>
<title>Ranked Bandits in Metric Spaces: Learning Diverse Rankings over Large Document Collections (Paper)</title>
<description>@article{14:14,author={Aleksandrs Slivkins and Filip Radlinski and Sreenivas Gollapudi}, Title={Ranked Bandits in Metric Spaces: Learning Diverse Rankings over Large Document Collections},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/slivkins13a/slivkins13a.pdf}}</description>
<link>https://academictorrents.com/download/07eaf4e61b499e738b1283822e747bdb6c822993</link>
</item>
<item>
<title>Sparse Activity and Sparse Connectivity in Supervised Learning (Paper)</title>
<description>@article{14:34,author={Markus Thom and Gnther Palm}, Title={Sparse Activity and Sparse Connectivity in Supervised Learning},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/thom13a/thom13a.pdf}}</description>
<link>https://academictorrents.com/download/4185bdbd907ae4cfa646f255c79f7382fdb0b3a6</link>
</item>
<item>
<title>Classifying With Confidence From Incomplete Information (Paper)</title>
<description>@article{14:113,author={Nathan Parrish and Hyrum S. Anderson and Maya R. Gupta and Dun Yu Hsiao}, Title={Classifying With Confidence From Incomplete Information},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/parrish13a/parrish13a.pdf}}</description>
<link>https://academictorrents.com/download/1626b334c3611184069e15f2d4a38d284c559405</link>
</item>
<item>
<title>Learning Theory Analysis for Association Rules and Sequential Event Prediction (Paper)</title>
<description>@article{14:109,author={Cynthia Rudin and Benjamin Letham and David Madigan}, Title={Learning Theory Analysis for Association Rules and Sequential Event Prediction},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/rudin13a/rudin13a.pdf}}</description>
<link>https://academictorrents.com/download/58947cb7a9cae9ed60c2a86027f988d5c645272f</link>
</item>
<item>
<title>Optimal Discovery with Probabilistic Expert Advice: Finite Time Analysis and Macroscopic Optimality (Paper)</title>
<description>@article{14:18,author={Sbastien Bubeck and Damien Ernst and Aurlien Garivier}, Title={Optimal Discovery with Probabilistic Expert Advice: Finite Time Analysis and Macroscopic Optimality},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/bubeck13a/bubeck13a.pdf}}</description>
<link>https://academictorrents.com/download/6d1c604147dd7bd88d31a8ca7415bc63ea71f2f5</link>
</item>
<item>
<title>Fast MCMC Sampling for Markov Jump Processes and Extensions (Paper)</title>
<description>@article{14:104,author={Vinayak Rao and Yee Whye Teh}, Title={Fast MCMC Sampling for Markov Jump Processes and Extensions},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/rao13a/rao13a.pdf}}</description>
<link>https://academictorrents.com/download/f25629fe4d9c718ae5c024e4c4abe514985c4429</link>
</item>
<item>
<title>A Least-squares Approach to Direct Importance Estimation (Paper)</title>
<description>@article{10:48,author={Takafumi Kanamori and Shohei Hido and Masashi Sugiyama}, Title={A Least-squares Approach to Direct Importance Estimation},journal={Journal of Machine Learning Research},volume={10}, url={http://www.jmlr.org/papers/volume10/kanamori09a/kanamori09a.pdf}}</description>
<link>https://academictorrents.com/download/1052dae93cf2dfdee284c06e36384b0c120508ee</link>
</item>
<item>
<title>One-shot Learning Gesture Recognition from RGB-D Data Using Bag of Features (Paper)</title>
<description>@article{14:79,author={Jun Wan and Qiuqi Ruan and Wei Li and Shuang Deng}, Title={One-shot Learning Gesture Recognition from RGB-D Data Using Bag of Features},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/wan13a/wan13a.pdf}}</description>
<link>https://academictorrents.com/download/14e4b3fffcbf8c4e2497ab35c3ea0c946d16b433</link>
</item>
<item>
<title>Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels (Paper)</title>
<description>@article{14:119,author={Kenji Fukumizu and Le Song and Arthur Gretton}, Title={Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/fukumizu13a/fukumizu13a.pdf}}</description>
<link>https://academictorrents.com/download/8e407b6e1ef0d8ab9719e4370dab098e5a91f3df</link>
</item>
<item>
<title>Dimension Independent Similarity Computation (Paper)</title>
<description>@article{14:51,author={Reza Bosagh Zadeh and Ashish Goel}, Title={Dimension Independent Similarity Computation},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/bosagh-zadeh13a/bosagh-zadeh13a.pdf}}</description>
<link>https://academictorrents.com/download/575f027a9c75e3447aae3454e06ad507d974b218</link>
</item>
<item>
<title>Ranking Forests (Paper)</title>
<description>@article{14:2,author={Stphan Clmenon and Marine Depecker and Nicolas Vayatis}, Title={Ranking Forests},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/clemencon13a/clemencon13a.pdf}}</description>
<link>https://academictorrents.com/download/647b5acb68f125d42953e0a3b64618d8e49ef188</link>
</item>
<item>
<title>Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models (Paper)</title>
<description>@article{14:4,author={Aapo Hyvrinen and Stephen M. Smith}, Title={Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/hyvarinen13a/hyvarinen13a.pdf}}</description>
<link>https://academictorrents.com/download/a7fefbb22f4272f4874df581d31c9f4c999d6fe3</link>
</item>
<item>
<title>On the Convergence of Maximum Variance Unfolding (Paper)</title>
<description>@article{14:55,author={Ery Arias-Castro and Bruno Pelletier}, Title={On the Convergence of Maximum Variance Unfolding},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/arias-castro13a/arias-castro13a.pdf}}</description>
<link>https://academictorrents.com/download/7724e3ac4793964830bbb1197c1d4b8055077eb4</link>
</item>
<item>
<title>Differential Privacy for Functions and Functional Data (Paper)</title>
<description>@article{14:22,author={Rob Hall and Alessandro Rinaldo and Larry Wasserman}, Title={Differential Privacy for Functions and Functional Data},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/hall13a/hall13a.pdf}}</description>
<link>https://academictorrents.com/download/7c41af39ec87531026ca7613e292cb9fbadcfbb9</link>
</item>
<item>
<title>Conjugate Relation between Loss Functions and Uncertainty Sets in Classification Problems (Paper)</title>
<description>@article{14:46,author={Takafumi Kanamori and Akiko Takeda and Taiji Suzuki}, Title={Conjugate Relation between Loss Functions and Uncertainty Sets in Classification Problems},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/kanamori13a/kanamori13a.pdf}}</description>
<link>https://academictorrents.com/download/2ba79fabb855edbf6822f941d893e5d59491f585</link>
</item>
<item>
<title>Divvy: Fast and Intuitive Exploratory Data Analysis (Paper)</title>
<description>@article{14:99,author={Joshua M. Lewis and Virginia R. de Sa and Laurens van der Maaten}, Title={Divvy: Fast and Intuitive Exploratory Data Analysis},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/lewis13a/lewis13a.pdf}}</description>
<link>https://academictorrents.com/download/f336d8efafd0aaeae8b622e29b1b95bf3d22597d</link>
</item>
<item>
<title>CODA: High Dimensional Copula Discriminant Analysis (Paper)</title>
<description>@article{14:20,author={Fang Han and Tuo Zhao and Han Liu}, Title={CODA: High Dimensional Copula Discriminant Analysis},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/han13a/han13a.pdf}}</description>
<link>https://academictorrents.com/download/65253d2fb4e703172a02fed3a3370fdc617e22ca</link>
</item>
<item>
<title>Using Symmetry and Evolutionary Search to Minimize Sorting Networks (Paper)</title>
<description>@article{14:10,author={Vinod K. Valsalam and Risto Miikkulainen}, Title={Using Symmetry and Evolutionary Search to Minimize Sorting Networks},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/valsalam13a/valsalam13a.pdf}}</description>
<link>https://academictorrents.com/download/681dd00655f3a45ecf9a46a8bf5f3602bfe9292a</link>
</item>
<item>
<title>A Framework for Evaluating Approximation Methods for Gaussian Process Regression (Paper)</title>
<description>@article{14:11,author={Krzysztof Chalupka and Christopher K. I. Williams and Iain Murray}, Title={A Framework for Evaluating Approximation Methods for Gaussian Process Regression},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/chalupka13a/chalupka13a.pdf}}</description>
<link>https://academictorrents.com/download/6d758dd0a91c0b6fd19b560b21f7af83e60f9de3</link>
</item>
<item>
<title>Bayesian Nonparametric Hidden Semi-Markov Models (Paper)</title>
<description>@article{14:21,author={Matthew J. Johnson and Alan S. Willsky}, Title={Bayesian Nonparametric Hidden Semi-Markov Models},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/johnson13a/johnson13a.pdf}}</description>
<link>https://academictorrents.com/download/b36dbb5196a73a82affdf3e546b7fa51ba60bb52</link>
</item>
<item>
<title>Stochastic Variational Inference (Paper)</title>
<description>@article{14:41,author={Matthew D. Hoffman and David M. Blei and Chong Wang and John Paisley}, Title={Stochastic Variational Inference},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/hoffman13a/hoffman13a.pdf}}</description>
<link>https://academictorrents.com/download/20a05bcb487cd0d3677f086c964d3e0059537dee</link>
</item>
<item>
<title>Comment on "Robustness and Regularization of Support Vector Machines" by H. Xu et al. (Journal of Machine Learning Research, vol. 10, pp. 1485-1510, 2009) (Paper)</title>
<description>@article{14:110,author={Yahya Forghani and Hadi Sadoghi}, Title={Comment on "Robustness and Regularization of Support Vector Machines" by H. Xu et al. (Journal of Machine Learning Research, vol. 10, pp. 1485-1510, 2009)},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/forghani13a/forghani13a.pdf}}</description>
<link>https://academictorrents.com/download/d3fa0705e62cc295e0d843df0907221f026ef597</link>
</item>
<item>
<title>On the Mutual Nearest Neighbors Estimate in Regression (Paper)</title>
<description>@article{14:74,author={Arnaud Guyader and Nick Hengartner}, Title={On the Mutual Nearest Neighbors Estimate in Regression},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/guyader13a/guyader13a.pdf}}</description>
<link>https://academictorrents.com/download/f4682214947aed9fd99205372630202aa1ae315f</link>
</item>
<item>
<title>Training Energy-Based Models for Time-Series Imputation (Paper)</title>
<description>@article{14:85,author={Philmon Brakel and Dirk Stroobandt and Benjamin Schrauwen}, Title={Training Energy-Based Models for Time-Series Imputation},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/brakel13a/brakel13a.pdf}}</description>
<link>https://academictorrents.com/download/0c162286c06b90e38d7218d05b5112eda0f1a1c1</link>
</item>
<item>
<title>Greedy Feature Selection for Subspace Clustering (Paper)</title>
<description>@article{14:77,author={Eva L. Dyer and Aswin C. Sankaranarayanan and Richard G. Baraniuk}, Title={Greedy Feature Selection for Subspace Clustering},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/dyer13a/dyer13a.pdf}}</description>
<link>https://academictorrents.com/download/b796e5aa53966f8ce62ee9a365f081df7d7be9cf</link>
</item>
<item>
<title>Query Induction with Schema-Guided Pruning Strategies (Paper)</title>
<description>@article{14:30,author={Joachim Niehren and Jrme Champavre and Aurlien Lemay and Rmi Gilleron}, Title={Query Induction with Schema-Guided Pruning Strategies},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/niehren13a/niehren13a.pdf}}</description>
<link>https://academictorrents.com/download/4217eafb21e63fbea07b82c407c23b1348574bd2</link>
</item>
<item>
<title>Convex and Scalable Weakly Labeled SVMs (Paper)</title>
<description>@article{14:66,author={Yu-Feng Li and Ivor W. Tsang and James T. Kwok and Zhi-Hua Zhou}, Title={Convex and Scalable Weakly Labeled SVMs},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/li13a/li13a.pdf}}</description>
<link>https://academictorrents.com/download/eb3bb1fc339cbee797f5d00a54ab05711852cc34</link>
</item>
<item>
<title>MLPACK: A Scalable C++ Machine Learning Library (Paper)</title>
<description>@article{14:25,author={Ryan R. Curtin and James R. Cline and N. P. Slagle and William B. March and Parikshit Ram and Nishant A. Mehta and Alexander G. Gray}, Title={MLPACK: A Scalable C++ Machine Learning Library},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/curtin13a/curtin13a.pdf}}</description>
<link>https://academictorrents.com/download/421ca1dc8f130655ce397a1c8debc783c02cbe36</link>
</item>
<item>
<title>Cluster Analysis: Unsupervised Learning via Supervised Learning with a Non-convex Penalty (Paper)</title>
<description>@article{14:59,author={Wei Pan and Xiaotong Shen and Binghui Liu}, Title={Cluster Analysis: Unsupervised Learning via Supervised Learning with a Non-convex Penalty},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/pan13a/pan13a.pdf}}</description>
<link>https://academictorrents.com/download/993186991db28b9dc900b79d8f0ebadd2cbc52bd</link>
</item>
<item>
<title>Efficient Active Learning of Halfspaces: An Aggressive Approach (Paper)</title>
<description>@article{14:80,author={Alon Gonen and Sivan Sabato and Shai Shalev-Shwartz}, Title={Efficient Active Learning of Halfspaces: An Aggressive Approach},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/gonen13a/gonen13a.pdf}}</description>
<link>https://academictorrents.com/download/d7c4adf97cb54719016ab5fd79b1b371a84515b5</link>
</item>
<item>
<title>A Binary-Classification-Based Metric between Time-Series Distributions and Its Use in Statistical and Learning Problems (Paper)</title>
<description>@article{14:87,author={Daniil Ryabko and Jrmie Mary}, Title={A Binary-Classification-Based Metric between Time-Series Distributions and Its Use in Statistical and Learning Problems},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/ryabko13a/ryabko13a.pdf}}</description>
<link>https://academictorrents.com/download/c2d3ecf0482fc560115374b49811ec987841f1f0</link>
</item>
<item>
<title>Nonparametric Sparsity and Regularization (Paper)</title>
<description>@article{14:53,author={Lorenzo Rosasco and Silvia Villa and Sofia Mosci and Matteo Santoro and Alessandro Verri}, Title={Nonparametric Sparsity and Regularization},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/rosasco13a/rosasco13a.pdf}}</description>
<link>https://academictorrents.com/download/e7495e1384e497a1fd0abb0681d5c385501c24aa</link>
</item>
<item>
<title>Language-Motivated Approaches to Action Recognition (Paper)</title>
<description>@article{14:67,author={Manavender R. Malgireddy and Ifeoma Nwogu and Venu Govindaraju}, Title={Language-Motivated Approaches to Action Recognition},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/malgireddy13a/malgireddy13a.pdf}}</description>
<link>https://academictorrents.com/download/0ec67ef0c5b2bd52c368fefa0adcd4d1c1acb6a4</link>
</item>
<item>
<title>A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion (Paper)</title>
<description>@article{14:115,author={Tony Cai and Wen-Xin Zhou}, Title={A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/cai13b/cai13b.pdf}}</description>
<link>https://academictorrents.com/download/9264f7bc4398f050d9bcf4de98fae7ea9084b21f</link>
</item>
<item>
<title>Finding Optimal Bayesian Networks Using Precedence Constraints (Paper)</title>
<description>@article{14:43,author={Pekka Parviainen and Mikko Koivisto}, Title={Finding Optimal Bayesian Networks Using Precedence Constraints},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/parviainen13a/parviainen13a.pdf}}</description>
<link>https://academictorrents.com/download/849050c3f0bc3e01c779052bdf08fa154bc15035</link>
</item>
<item>
<title>Risk Bounds of Learning Processes for Lvy Processes (Paper)</title>
<description>@article{14:12,author={Chao Zhang and Dacheng Tao}, Title={Risk Bounds of Learning Processes for Lvy Processes},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/zhang13a/zhang13a.pdf}}</description>
<link>https://academictorrents.com/download/6c4bc5c78e4ef49baa1f149efc17be6043c6ec80</link>
</item>
<item>
<title>GURLS: A Least Squares Library for Supervised Learning (Paper)</title>
<description>@article{14:101,author={Andrea Tacchetti and Pavan K. Mallapragada and Matteo Santoro and Lorenzo Rosasco}, Title={GURLS: A Least Squares Library for Supervised Learning},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/tacchetti13a/tacchetti13a.pdf}}</description>
<link>https://academictorrents.com/download/b4c0f6ea0506eeff72c634ec5fda84d1642e7bd2</link>
</item>
<item>
<title>Bayesian Canonical Correlation Analysis (Paper)</title>
<description>@article{14:31,author={Arto Klami and Seppo Virtanen and Samuel Kaski}, Title={Bayesian Canonical Correlation Analysis},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/klami13a/klami13a.pdf}}</description>
<link>https://academictorrents.com/download/eee108e08076ee344aa51cdfc9734c797120602e</link>
</item>
<item>
<title>The Rate of Convergence of AdaBoost (Paper)</title>
<description>@article{14:71,author={Indraneel Mukherjee and Cynthia Rudin and Robert E. Schapire}, Title={The Rate of Convergence of AdaBoost},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/mukherjee13b/mukherjee13b.pdf}}</description>
<link>https://academictorrents.com/download/ff0b5f0827dd3d3c6d8cda578c71eb14e96e72fa</link>
</item>
<item>
<title>The CAM Software for Nonnegative Blind Source Separation in R-Java (Paper)</title>
<description>@article{14:89,author={Niya Wang and Fan Meng and Li Chen and Subha Madhavan and Robert Clarke and Eric P. Hoffman and Jianhua Xuan and Yue Wang}, Title={The CAM Software for Nonnegative Blind Source Separation in R-Java},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/wang13d/wang13d.pdf}}</description>
<link>https://academictorrents.com/download/84f3cc29df08fb56897e273e4bb9ddb88826ddcd</link>
</item>
<item>
<title>Learning Theory Approach to Minimum Error Entropy Criterion (Paper)</title>
<description>@article{14:13,author={Ting Hu and Jun Fan and Qiang Wu and Ding-Xuan Zhou}, Title={Learning Theory Approach to Minimum Error Entropy Criterion},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/hu13a/hu13a.pdf}}</description>
<link>https://academictorrents.com/download/4be84fdbfbea612cf6152061b6e9bcabdb08d727</link>
</item>
<item>
<title>Segregating Event Streams and Noise with a Markov Renewal Process Model (Paper)</title>
<description>@article{14:68,author={Dan Stowell and Mark D. Plumbley}, Title={Segregating Event Streams and Noise with a Markov Renewal Process Model},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/stowell13a/stowell13a.pdf}}</description>
<link>https://academictorrents.com/download/914bd8a803ca859ce68c8f590a0c201bc4f392c5</link>
</item>
<item>
<title>Message-Passing Algorithms for Quadratic Minimization (Paper)</title>
<description>@article{14:70,author={Nicholas Ruozzi and Sekhar Tatikonda}, Title={Message-Passing Algorithms for Quadratic Minimization},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/ruozzi13a/ruozzi13a.pdf}}</description>
<link>https://academictorrents.com/download/264e2cd52f86ee907b6b4f3d433b2c5e21cb6c9c</link>
</item>
<item>
<title>Semi-Supervised Learning Using Greedy Max-Cut (Paper)</title>
<description>@article{14:24,author={Jun Wang and Tony Jebara and Shih-Fu Chang}, Title={Semi-Supervised Learning Using Greedy Max-Cut},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/wang13a/wang13a.pdf}}</description>
<link>https://academictorrents.com/download/86353516df61c721c88727c5e08102d62f5bb0c8</link>
</item>
<item>
<title>A Theory of Multiclass Boosting (Paper)</title>
<description>@article{14:15,author={Indraneel Mukherjee and Robert E. Schapire}, Title={A Theory of Multiclass Boosting},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/mukherjee13a/mukherjee13a.pdf}}</description>
<link>https://academictorrents.com/download/345fadbd720a0c19cceae72cd7bcbf3097e4709d</link>
</item>
<item>
<title>Multicategory Large-Margin Unified Machines (Paper)</title>
<description>@article{14:42,author={Chong Zhang and Yufeng Liu}, Title={Multicategory Large-Margin Unified Machines},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/liu13a/liu13a.pdf}}</description>
<link>https://academictorrents.com/download/f974c362bb4dd7389d57ca440ce30cbe6d4c1da2</link>
</item>
<item>
<title>Construction of Approximation Spaces for Reinforcement Learning (Paper)</title>
<description>@article{14:64,author={Wendelin Bhmer and Steffen Grnewlder and Yun Shen and Marek Musial and Klaus Obermayer}, Title={Construction of Approximation Spaces for Reinforcement Learning},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/boehmer13a/boehmer13a.pdf}}</description>
<link>https://academictorrents.com/download/84509a4a078fa60891ac06266b40a5e462e5aa21</link>
</item>
<item>
<title>Classifier Selection using the Predicate Depth (Paper)</title>
<description>@article{14:114,author={Ran Gilad-Bachrach and Christopher J.C. Burges}, Title={Classifier Selection using the Predicate Depth},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/gilad-bachrach13a/gilad-bachrach13a.pdf}}</description>
<link>https://academictorrents.com/download/214e8915b8577100002a71fa3d47a86b1a029909</link>
</item>
<item>
<title>Derivative Estimation with Local Polynomial Fitting (Paper)</title>
<description>@article{14:9,author={Kris De Brabanter and Jos De Brabanter and Bart De Moor and Irne Gijbels}, Title={Derivative Estimation with Local Polynomial Fitting},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/debrabanter13a/debrabanter13a.pdf}}</description>
<link>https://academictorrents.com/download/229007c16859c349db75d9291007dc6d9164a3d2</link>
</item>
<item>
<title>PC Algorithm for Nonparanormal Graphical Models (Paper)</title>
<description>@article{14:106,author={Naftali Harris and Mathias Drton}, Title={PC Algorithm for Nonparanormal Graphical Models},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/harris13a/harris13a.pdf}}</description>
<link>https://academictorrents.com/download/4b00a46b4e282e2374c3828c5c0afe0d52cf106e</link>
</item>
<item>
<title>Distribution-Dependent Sample Complexity of Large Margin Learning (Paper)</title>
<description>@article{14:65,author={Sivan Sabato and Nathan Srebro and Naftali Tishby}, Title={Distribution-Dependent Sample Complexity of Large Margin Learning},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/sabato13a/sabato13a.pdf}}</description>
<link>https://academictorrents.com/download/f6d27117b042204a40e7e190329034d663c1b108</link>
</item>
<item>
<title>Multi-Stage Multi-Task Feature Learning (Paper)</title>
<description>@article{14:92,author={Pinghua Gong and Jieping Ye and Changshui Zhang}, Title={Multi-Stage Multi-Task Feature Learning},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/gong13a/gong13a.pdf}}</description>
<link>https://academictorrents.com/download/3e14552bdb167b2b7a22de8cb7dd0c96de0097f7</link>
</item>
<item>
<title>Sparse Single-Index Model (Paper)</title>
<description>@article{14:8,author={Pierre Alquier and Grard Biau}, Title={Sparse Single-Index Model},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/alquier13a/alquier13a.pdf}}</description>
<link>https://academictorrents.com/download/61059f50d1a161c60b8c1ea44ad0e8d3090bdd1b</link>
</item>
<item>
<title>Random Spanning Trees and the Prediction of Weighted Graphs (Paper)</title>
<description>@article{14:39,author={Nicol Cesa-Bianchi and Claudio Gentile and Fabio Vitale and Giovanni Zappella}, Title={Random Spanning Trees and the Prediction of Weighted Graphs},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/cesa-bianchi13a/cesa-bianchi13a.pdf}}</description>
<link>https://academictorrents.com/download/66ed1109aa9929c9eb1c24516b32955b7150b2f3</link>
</item>
<item>
<title>A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics (Paper)</title>
<description>@article{14:19,author={Herv Frezza-Buet and Matthieu Geist}, Title={A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/frezza-buet13a/frezza-buet13a.pdf}}</description>
<link>https://academictorrents.com/download/8e8a341c6948e0f1e4f70fd0386aa5a408bdb07f</link>
</item>
<item>
<title>Optimally Fuzzy Temporal Memory (Paper)</title>
<description>@article{14:120,author={Karthik H. Shankar and Marc W. Howard}, Title={Optimally Fuzzy Temporal Memory},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/shankar13a/shankar13a.pdf}}</description>
<link>https://academictorrents.com/download/d9fd9cf32c98c6065ee9f1bcfcefe1eb31bd0153</link>
</item>
<item>
<title>Fast Generalized Subset Scan for Anomalous Pattern Detection (Paper)</title>
<description>@article{14:49,author={Edward McFowland III and Skyler Speakman and Daniel B. Neill}, Title={Fast Generalized Subset Scan for Anomalous Pattern Detection},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/mcfowland13a/mcfowland13a.pdf}}</description>
<link>https://academictorrents.com/download/d0b31ee6d5931a855f800299083e6e7aa3941f17</link>
</item>
<item>
<title>A Risk Comparison of Ordinary Least Squares vs Ridge Regression (Paper)</title>
<description>@article{14:47,author={Paramveer S. Dhillon and Dean P.  Foster and Sham M.  Kakade and Lyle H. Ungar}, Title={A Risk Comparison of Ordinary Least Squares vs Ridge Regression},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/dhillon13a/dhillon13a.pdf}}</description>
<link>https://academictorrents.com/download/6c43ba1182eb57633acdfd0ff0dff42d96d34abc</link>
</item>
<item>
<title>QuantMiner for Mining Quantitative Association Rules (Paper)</title>
<description>@article{14:98,author={Ansaf Salleb-Aouissi and Christel Vrain and Cyril Nortet and Xiangrong Kong and Vivek Rathod and Daniel Cassard}, Title={QuantMiner for Mining Quantitative Association Rules},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/salleb-aouissi13a/salleb-aouissi13a.pdf}}</description>
<link>https://academictorrents.com/download/e2d6ac42e4b4e038afc01aab5c8419bf75f3f85a</link>
</item>
<item>
<title>Consistent Selection of Tuning Parameters via Variable Selection Stability (Paper)</title>
<description>@article{14:108,author={Wei Sun and Junhui Wang and Yixin Fang}, Title={Consistent Selection of Tuning Parameters via Variable Selection Stability},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/sun13b/sun13b.pdf}}</description>
<link>https://academictorrents.com/download/56b0927f2f3ac7afcd7813e81edcd542582667f2</link>
</item>
<item>
<title>Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows (Paper)</title>
<description>@article{14:76,author={Julien Mairal and Bin Yu}, Title={Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/mairal13a/mairal13a.pdf}}</description>
<link>https://academictorrents.com/download/b863eefbdcc6404f3be91c3d095435a1b96f5b70</link>
</item>
<item>
<title>Machine Learning with Operational Costs (Paper)</title>
<description>@article{14:62,author={Theja Tulabandhula and Cynthia Rudin}, Title={Machine Learning with Operational Costs},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/tulabandhula13a/tulabandhula13a.pdf}}</description>
<link>https://academictorrents.com/download/15efe86562dcb326f78d160eb9bd2526a5e4a431</link>
</item>
<item>
<title>Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs (Paper)</title>
<description>@article{14:57,author={Matthew J. Urry and Peter Sollich}, Title={Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/urry13a/urry13a.pdf}}</description>
<link>https://academictorrents.com/download/ace4c246b027b1f9b6e8c1754308eb1cda6c898e</link>
</item>
<item>
<title>Learning Bilinear Model for Matching Queries and Documents (Paper)</title>
<description>@article{14:78,author={Wei Wu and Zhengdong  Lu and Hang Li}, Title={Learning Bilinear Model for Matching Queries and Documents},journal={Journal of Machine Learning Research},volume={14}, url={http://jmlr.org/papers/volume14/wu13a/wu13a.pdf}}</description>
<link>https://academictorrents.com/download/afbdf200cac57471377e6b8398f4ce71159f7425</link>
</item>
<item>
<title>Stress Functions for Nonlinear Dimension Reduction, Proximity Analysis, and Graph Drawing (Paper)</title>
<description>@article{14:35,author={Lisha Chen and Andreas Buja}, Title={Stress Functions for Nonlinear Dimension Reduction, Proximity Analysis, and Graph Drawing},journal={Journal of Machine Learning Research},volume={14}, url={http://www.jmlr.org/papers/volume14/chen13a/chen13a.pdf}}</description>
<link>https://academictorrents.com/download/78f800a01c5a4f423e355d96868684c18ca6bd37</link>
</item>
</channel>
</rss>
