TYPE,NAME,INFOHASH,SIZEBYTES,MIRRORS,DOWNLOADERS,TIMESCOMPLETED,DATEADDED,DATEMODIFIED
Paper,"A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics",8e8a341c6948e0f1e4f70fd0386aa5a408bdb07f,70368,1,0,65,1398650160,0
Paper,"Optimally Fuzzy Temporal Memory",d9fd9cf32c98c6065ee9f1bcfcefe1eb31bd0153,674312,1,0,34,1398650160,0
Paper,"Fast Generalized Subset Scan for Anomalous Pattern Detection",d0b31ee6d5931a855f800299083e6e7aa3941f17,2755573,0,0,30,1398650160,0
Paper,"A Risk Comparison of Ordinary Least Squares vs Ridge Regression",6c43ba1182eb57633acdfd0ff0dff42d96d34abc,189762,0,0,43,1398650160,0
Paper,"QuantMiner for Mining Quantitative Association Rules",e2d6ac42e4b4e038afc01aab5c8419bf75f3f85a,319048,0,0,40,1398650160,0
Paper,"Consistent Selection of Tuning Parameters via Variable Selection Stability",56b0927f2f3ac7afcd7813e81edcd542582667f2,251371,0,0,27,1398650160,0
Paper,"Supervised Feature Selection in Graphs with Path Coding Penalties and Network Flows",b863eefbdcc6404f3be91c3d095435a1b96f5b70,373945,0,0,31,1398650160,0
Paper,"Machine Learning with Operational Costs",15efe86562dcb326f78d160eb9bd2526a5e4a431,824494,0,0,41,1398650160,0
Paper,"Random Walk Kernels and Learning Curves for Gaussian Process Regression on Random Graphs",ace4c246b027b1f9b6e8c1754308eb1cda6c898e,517105,0,0,28,1398650160,0
Paper,"Learning Bilinear Model for Matching Queries and Documents",afbdf200cac57471377e6b8398f4ce71159f7425,350476,0,0,30,1398650160,0
Paper,"Stress Functions for Nonlinear Dimension Reduction, Proximity Analysis, and Graph Drawing",78f800a01c5a4f423e355d96868684c18ca6bd37,593035,1,0,113,1398650160,0
Paper,"The Rate of Convergence of AdaBoost",ff0b5f0827dd3d3c6d8cda578c71eb14e96e72fa,314876,0,0,30,1398650161,0
Paper,"The CAM Software for Nonnegative Blind Source Separation in R-Java",84f3cc29df08fb56897e273e4bb9ddb88826ddcd,637864,0,0,54,1398650161,0
Paper,"Learning Theory Approach to Minimum Error Entropy Criterion",4be84fdbfbea612cf6152061b6e9bcabdb08d727,194956,0,0,31,1398650161,0
Paper,"Segregating Event Streams and Noise with a Markov Renewal Process Model",914bd8a803ca859ce68c8f590a0c201bc4f392c5,744310,1,0,33,1398650161,0
Paper,"Message-Passing Algorithms for Quadratic Minimization",264e2cd52f86ee907b6b4f3d433b2c5e21cb6c9c,264041,1,0,42,1398650161,0
Paper,"Semi-Supervised Learning Using Greedy Max-Cut",86353516df61c721c88727c5e08102d62f5bb0c8,1493677,0,0,32,1398650161,0
Paper,"A Theory of Multiclass Boosting",345fadbd720a0c19cceae72cd7bcbf3097e4709d,2956784,0,0,33,1398650161,0
Paper,"Multicategory Large-Margin Unified Machines",f974c362bb4dd7389d57ca440ce30cbe6d4c1da2,612665,0,0,26,1398650161,0
Paper,"Construction of Approximation Spaces for Reinforcement Learning",84509a4a078fa60891ac06266b40a5e462e5aa21,778545,0,0,31,1398650161,0
Paper,"Classifier Selection using the Predicate Depth",214e8915b8577100002a71fa3d47a86b1a029909,243217,0,0,29,1398650161,0
Paper,"Derivative Estimation with Local Polynomial Fitting",229007c16859c349db75d9291007dc6d9164a3d2,590779,0,0,10,1398650161,0
Paper,"PC Algorithm for Nonparanormal Graphical Models",4b00a46b4e282e2374c3828c5c0afe0d52cf106e,177771,3,0,49,1398650161,0
Paper,"Distribution-Dependent Sample Complexity of Large Margin Learning",f6d27117b042204a40e7e190329034d663c1b108,335735,0,0,28,1398650161,0
Paper,"Multi-Stage Multi-Task Feature Learning",3e14552bdb167b2b7a22de8cb7dd0c96de0097f7,303999,0,0,32,1398650161,0
Paper,"Sparse Single-Index Model",61059f50d1a161c60b8c1ea44ad0e8d3090bdd1b,321557,0,0,33,1398650161,0
Paper,"Random Spanning Trees and the Prediction of Weighted Graphs",66ed1109aa9929c9eb1c24516b32955b7150b2f3,369540,2,0,37,1398650161,0
Paper,"Training Energy-Based Models for Time-Series Imputation",0c162286c06b90e38d7218d05b5112eda0f1a1c1,419200,1,0,35,1398650162,0
Paper,"Greedy Feature Selection for Subspace Clustering",b796e5aa53966f8ce62ee9a365f081df7d7be9cf,956724,0,0,28,1398650162,0
Paper,"Query Induction with Schema-Guided Pruning Strategies",4217eafb21e63fbea07b82c407c23b1348574bd2,424419,0,0,34,1398650162,0
Paper,"Convex and Scalable Weakly Labeled SVMs",eb3bb1fc339cbee797f5d00a54ab05711852cc34,1026847,0,0,29,1398650162,0
Paper,"MLPACK: A Scalable C++ Machine Learning Library",421ca1dc8f130655ce397a1c8debc783c02cbe36,68230,0,0,49,1398650162,0
Paper,"Cluster Analysis: Unsupervised Learning via Supervised Learning with a Non-convex Penalty",993186991db28b9dc900b79d8f0ebadd2cbc52bd,240325,0,0,43,1398650162,0
Paper,"Efficient Active Learning of Halfspaces: An Aggressive Approach",d7c4adf97cb54719016ab5fd79b1b371a84515b5,2894421,0,0,29,1398650162,0
Paper,"A Binary-Classification-Based Metric between Time-Series Distributions and Its Use in Statistical and Learning Problems",c2d3ecf0482fc560115374b49811ec987841f1f0,207382,0,0,32,1398650162,0
Paper,"Nonparametric Sparsity and Regularization",e7495e1384e497a1fd0abb0681d5c385501c24aa,612850,0,0,32,1398650162,0
Paper,"Language-Motivated Approaches to Action Recognition",0ec67ef0c5b2bd52c368fefa0adcd4d1c1acb6a4,1740589,1,0,45,1398650162,0
Paper,"A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion",9264f7bc4398f050d9bcf4de98fae7ea9084b21f,269057,0,0,24,1398650162,0
Paper,"Finding Optimal Bayesian Networks Using Precedence Constraints",849050c3f0bc3e01c779052bdf08fa154bc15035,343106,0,0,26,1398650162,0
Paper,"Risk Bounds of Learning Processes for Lvy Processes",6c4bc5c78e4ef49baa1f149efc17be6043c6ec80,273289,0,0,30,1398650162,0
Paper,"GURLS: A Least Squares Library for Supervised Learning",b4c0f6ea0506eeff72c634ec5fda84d1642e7bd2,76030,0,0,37,1398650162,0
Paper,"Bayesian Canonical Correlation Analysis",eee108e08076ee344aa51cdfc9734c797120602e,515468,0,0,35,1398650162,0
Paper,"Kernel Bayes' Rule: Bayesian Inference with Positive Definite Kernels",8e407b6e1ef0d8ab9719e4370dab098e5a91f3df,329174,0,0,39,1398650163,0
Paper,"Dimension Independent Similarity Computation",575f027a9c75e3447aae3454e06ad507d974b218,190022,0,0,26,1398650163,0
Paper,"Ranking Forests",647b5acb68f125d42953e0a3b64618d8e49ef188,1006171,0,0,29,1398650163,0
Paper,"Pairwise Likelihood Ratios for Estimation of Non-Gaussian Structural Equation Models",a7fefbb22f4272f4874df581d31c9f4c999d6fe3,1163343,0,0,35,1398650163,0
Paper,"On the Convergence of Maximum Variance Unfolding",7724e3ac4793964830bbb1197c1d4b8055077eb4,219460,0,0,30,1398650163,0
Paper,"Differential Privacy for Functions and Functional Data",7c41af39ec87531026ca7613e292cb9fbadcfbb9,314746,0,0,28,1398650163,0
Paper,"Conjugate Relation between Loss Functions and Uncertainty Sets in Classification Problems",2ba79fabb855edbf6822f941d893e5d59491f585,535153,0,0,43,1398650163,0
Paper,"Divvy: Fast and Intuitive Exploratory Data Analysis",f336d8efafd0aaeae8b622e29b1b95bf3d22597d,276717,1,0,36,1398650163,0
Paper,"CODA: High Dimensional Copula Discriminant Analysis",65253d2fb4e703172a02fed3a3370fdc617e22ca,399297,0,0,27,1398650163,0
Paper,"Using Symmetry and Evolutionary Search to Minimize Sorting Networks",681dd00655f3a45ecf9a46a8bf5f3602bfe9292a,507961,0,0,32,1398650163,0
Paper,"A Framework for Evaluating Approximation Methods for Gaussian Process Regression",6d758dd0a91c0b6fd19b560b21f7af83e60f9de3,331766,0,0,33,1398650163,0
Paper,"Bayesian Nonparametric Hidden Semi-Markov Models",b36dbb5196a73a82affdf3e546b7fa51ba60bb52,586134,0,0,44,1398650163,0
Paper,"Stochastic Variational Inference",20a05bcb487cd0d3677f086c964d3e0059537dee,397775,0,0,30,1398650163,0
Paper,"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)",d3fa0705e62cc295e0d843df0907221f026ef597,41090,0,0,27,1398650163,0
Paper,"On the Mutual Nearest Neighbors Estimate in Regression",f4682214947aed9fd99205372630202aa1ae315f,142780,0,0,25,1398650163,0
Paper,"Orange: Data Mining Toolbox in Python",78e7fe1bb1b432f24c4e0226a25d02c2a8ca60c2,63403,5,0,163,1398650164,0
Paper,"Multivariate Convex Regression with Adaptive Partitioning",0e27179d76982da01c8341a89719f8f227ee9ae4,1729398,1,0,38,1398650164,0
Paper,"Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination(Special Topic on Model Selection)",a6f1ed86b6f94fa6834470612936ff64da1a8ae9,200941,0,0,30,1398650164,0
Paper,"Dynamic Affine-Invariant Shape-Appearance Handshape Features and Classification in Sign Language Videos",6a0ba63fce87c30813082fc424ed2328836b7bbf,3980870,0,0,31,1398650164,0
Paper,"Distributions of Angles in Random Packing on Spheres",2cef7ef506fad29fe636bc6c67d7dd3045bc011b,276012,0,0,34,1398650164,0
Paper,"Alleviating Naive Bayes Attribute Independence Assumption by Attribute Weighting",13a66360265771ac37ad4fe29881cebb33a764e7,442448,0,0,30,1398650164,0
Paper,"Greedy Sparsity-Constrained Optimization",3586e9c854fd5ee46fee11267c739f656e5cbafc,362499,0,0,28,1398650164,0
Paper,"Lower Bounds and Selectivity of Weak-Consistent Policies in Stochastic Multi-Armed Bandit Problem",9e40b4952217ba1d7e8995563dfa9bb7ed456506,200238,0,0,25,1398650164,0
Paper,"The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs",0e020decfe281838b7b7509664c72c4fc51c4cb0,1532710,0,0,28,1398650164,0
Paper,"BudgetedSVM: A Toolbox for Scalable SVM Approximations",b15610978ec89ae1a633e4f4a9dca94a0e22815a,74781,0,0,27,1398650164,0
Paper,"Ranked Bandits in Metric Spaces: Learning Diverse Rankings over Large Document Collections",07eaf4e61b499e738b1283822e747bdb6c822993,325553,0,0,30,1398650164,0
Paper,"Sparse Activity and Sparse Connectivity in Supervised Learning",4185bdbd907ae4cfa646f255c79f7382fdb0b3a6,526246,0,0,32,1398650164,0
Paper,"Classifying With Confidence From Incomplete Information",1626b334c3611184069e15f2d4a38d284c559405,1091459,0,0,32,1398650164,0
Paper,"Learning Theory Analysis for Association Rules and Sequential Event Prediction",58947cb7a9cae9ed60c2a86027f988d5c645272f,1985899,0,0,30,1398650164,0
Paper,"Optimal Discovery with Probabilistic Expert Advice: Finite Time Analysis and Macroscopic Optimality",6d1c604147dd7bd88d31a8ca7415bc63ea71f2f5,327203,0,0,27,1398650164,0
Paper,"Fast MCMC Sampling for Markov Jump Processes and Extensions",f25629fe4d9c718ae5c024e4c4abe514985c4429,393640,0,0,28,1398650164,0
Paper,"A Least-squares Approach to Direct Importance Estimation",1052dae93cf2dfdee284c06e36384b0c120508ee,849232,0,0,34,1398650164,0
Paper,"One-shot Learning Gesture Recognition from RGB-D Data Using Bag of Features",14e4b3fffcbf8c4e2497ab35c3ea0c946d16b433,2952074,0,0,33,1398650164,0
Paper,"Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors",7cb135851a967364a44b500ef1382524390f5fd7,174470,0,0,49,1398650165,0
Paper,"Quasi-Newton Method: A New Direction",56fa80cbecf20bd53066d0cbd4b84d5224236912,297809,0,0,45,1398650165,0
Paper,"Data-driven Calibration of Penalties for Least-Squares Regression",fe4da40a6b0027d3ae8c9531cbe9224014e56af1,262751,0,0,31,1398650165,0
Paper,"Analysis of Perceptron-Based Active Learning",dcff855a3bbf542f6784ee85764cb96ac13c6e55,176009,0,0,37,1398650165,0
Paper,"Properties of Monotonic Effects on Directed Acyclic Graphs",78411eabf3e1484d3bd97b2b03fe8cbfb3316859,181590,0,0,28,1398650165,0
Paper,"CarpeDiem: Optimizing the Viterbi Algorithm and Applications to Supervised Sequential Learning",59c4f8d17a1b6bf8cb10135b87dbd2309ef7c188,1149428,0,0,32,1398650165,0
Paper,"Truncated Power Method for Sparse Eigenvalue Problems",807e9ca4e5e7303780944acba61182f8f81717d0,645499,0,0,44,1398650165,0
Paper,"Sparse Matrix Inversion with Scaled Lasso",46e403867091a86d6ccc31f9808beedf52adf803,313345,0,0,26,1398650165,0
Paper,"Robustness and Regularization of Support Vector Machines",c213084c164b0d7f80c6b816ceee199b697e2dc3,189265,0,0,45,1398650165,0
Paper,"Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models",c039fcb1cac9bff23da0ac663e0d1bee6f4aae9c,564841,0,0,28,1398650165,0
Paper,"Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions",2a0b3a40040592aef9c4c747bd0e4997360d8e3d,314362,1,0,41,1398650165,0
Paper,"Keep It Simple And Sparse: Real-Time Action Recognition",0216db9a867c799c7da89b3a052c4a6293b4d7c8,1869241,0,0,47,1398650165,0
Paper,"Online Learning with Sample Path Constraints",03465825ecd565378cacfa979f041b0d8f2593be,192136,0,0,37,1398650165,0
Paper,"Reinforcement Learning in Finite MDPs: PAC Analysis",2cd4976d263fd6e5d4b1f8e2f22438b1b82e9cde,272554,0,0,35,1398650165,0
Paper,"A Survey of Accuracy Evaluation Metrics of Recommendation Tasks",b31f8310f96f1b3563bdb7a3d11878711b63b000,267620,0,0,29,1398650165,0
Paper,"Sub-Local Constraint-Based Learning of Bayesian Networks Using A Joint Dependence Criterion",3cc32b00695bb7bb972bbcdf8e75fb666463db86,614808,0,0,46,1398650166,0
Paper,"Global Analytic Solution of Fully-observed Variational Bayesian Matrix Factorization",14361906a005e3523b5470dafe249c2b873cdcca,507623,0,0,31,1398650166,0
Paper,"Stable and Efficient Gaussian Process Calculations",e57b2069c47c25a5bb00f886c925a305eceef20d,246968,0,0,27,1398650166,0
Paper,"Gaussian Kullback-Leibler Approximate Inference",92db2e5b8a0e24d94e5234e948049f6bc6cc9438,922451,0,0,32,1398650166,0
Paper,"Similarity-based Clustering by Left-Stochastic Matrix Factorization",f2dec4ca78971464b95b3abad3505fd03b5c2cc1,317135,0,0,25,1398650166,0
Paper,"Hybrid MPIOpenMP Parallel Linear Support Vector Machine Training",cadd0708c887e5e73fceb9cbec3123f5e6150822,133809,0,0,31,1398650166,0
Paper,"Deterministic Error Analysis of Support Vector Regression and Related Regularized Kernel Methods",1ad9699408b1524336afed9ad9a4d04295f6b691,163771,1,0,32,1398650166,0
Paper,"Large-scale SVD and Manifold Learning",57bd784d8ae732ff60e330239ff43ef62c2c3abc,4354683,0,0,27,1398650166,0
Paper,"Algorithms for Discovery of Multiple Markov Boundaries",68408d7bda78720ac26f8e057d8f8cde9abd5718,831150,0,0,35,1398650166,0
Paper,"Similarity-based Classification: Concepts and Algorithms",bcdd07948277fb3b20b1727ef2104107a05995eb,8741840,0,0,54,1398650166,0
Paper,"Prediction With Expert Advice For The Brier Game",5839ddb77c57af03dd14e4b9da0cd7ce727664ae,1338728,0,0,28,1398650166,0
Paper,"The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models",3f017fc1ceb243bf9c912df7e9a81da265ff1122,676531,0,0,44,1398650166,0
Paper,"Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation",e5b57ee36c72734c8692c50356be672c12d35fed,1656995,0,0,30,1398650166,0
Paper,"Estimating Labels from Label Proportions",8dc40264fcbfb8c203cb0fd94fa207d0162c7c53,390111,0,0,28,1398650166,0
Paper,"Robust Process Discovery with Artificial Negative Events(Special Topic on Mining and Learning with Graphs and Relations)",25d166d65b19493fe44c29af341d71589179b2d3,499128,0,0,37,1398650166,0
Paper,"Fast Approximate kNN Graph Construction for High Dimensional Data via Recursive Lanczos Bisection",2a95276bf51b951ddafdf9681ff18ecba03b92ea,3239712,1,0,32,1398650166,0
Paper,"Lovasz theta function, SVMs and Finding Dense Subgraphs",67debaedcfd2ce2480e419ce3a44156135d38b8a,730305,0,0,29,1398650166,0
Paper,"Perturbation Corrections in Approximate Inference: Mixture Modelling Applications",503dcb099f984a19d76edd8303fa9915f4894edb,863458,0,0,29,1398650166,0
Paper,"Stationary-Sparse Causality Network Learning",7a3ecd1c3f81fb6a0dda78bc1c2ff8d87758d759,416785,0,0,22,1398650166,0
Paper,"Tapkee: An Efficient Dimension Reduction Library",ec09477ac7af2e3bb135819d83619f1a4fcc0c37,598465,0,0,26,1398650167,0
Paper,"Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising",76b9a4b27cd4f3436b349e438be1c1663abeebea,1394801,0,0,38,1398650167,0
Paper,"Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data(Machine Learning Open Source Software Paper)",e684c0edea6d7ec83fb16980bdcb7e502adef004,37216,1,0,168,1398650167,0
Paper,"Variational Algorithms for Marginal MAP",ef3fd853ccf3a0c8331ab9908d3e4befabf5ef80,665799,0,0,39,1398650167,0
Paper,"Distributed Algorithms for Topic Models",6a4b9d2eecc190f85a80adffe68ba52da77af94e,332885,0,0,34,1398650167,0
Paper,"Parallel Vector Field Embedding",e4a5f0b14499f2660ccfeb92ab3af65557fc2067,4100920,0,0,33,1398650167,0
Paper,"Multi-task Reinforcement Learning in Partially Observable Stochastic Environments",902bb50e342e15cafa431ff8c09f2df85b168c0f,670454,0,0,28,1398650167,0
Paper,"How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis",6588c6a4ac6f3af9467374146a433154c82a6ba1,1190557,0,0,38,1398650167,0
Paper,"Universal Kernel-Based Learning with Applications to Regular Languages(Special Topic on Mining and Learning with Graphs and Relations)",f7268a3b3b5c240236c163bda5d0bbcd6388a94a,282087,0,0,32,1398650167,0
Paper,"Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques",f620441db22049530cc97763ccc9bddd5769d61b,878323,0,0,52,1398650167,0
Paper,"Low-Rank Kernel Learning with Bregman Matrix Divergences",36fbeaf6bdd3f6743e3b9701d7dc4761275c4f96,390237,2,0,38,1398650167,0
Paper,"Learning Acyclic Probabilistic Circuits Using Test Paths",7791936dbe592270bf422723b26427053790de17,261886,0,0,24,1398650167,0
Paper,"Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning",214a7984eddbaf4f80683d2d3a25f7f937dfa209,377573,0,1,28,1398650167,0
Paper,"Learning Approximate Sequential Patterns for Classification",c0ddd60f3b29fb6c6dad16bfe87b2c4699d42c1b,199948,0,0,28,1398650167,0
Paper,"Combining Information Extraction Systems Using Voting and Stacked Generalization",7f914e88ed18d63b49f0cf8cf4878cb6d2b7b348,407661,0,0,36,1398650167,0
Paper,"Adaptive False Discovery Rate Control under Independence and Dependence",a6505f1745d9214d0fbc5bf66f7b790d8e8cc363,364223,0,0,26,1398650167,0
Paper,"Learning Linear Ranking Functions for Beam Search with Application to Planning",8c2e77b844696e7c0960ea4dd415192de1afc31e,354721,0,0,30,1398650167,0
Paper,"SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent",b34015056bd14ce7c9ef67cd8f4e209a8c7dc697,151450,0,0,25,1398650167,0
Paper,"Bayesian Network Structure Learning by Recursive Autonomy Identification",148196cd681145324b6e1ac5b92572ea388e1827,532303,0,0,32,1398650167,0
Paper,"Java-ML: A Machine Learning Library(Machine Learning Open Source Software Paper)",51f1fcb31e6a52e3ef2e30d60a9c7c03b82d8b3f,28916,2,0,135,1398650168,0
Paper,"Ultrahigh Dimensional Feature Selection: Beyond The Linear Model",58d5bf59fec3abe12ec451279938a4bc71ad6bfa,195894,0,0,25,1398650168,0
Paper,"MAGIC Summoning: Towards Automatic Suggesting and Testing of Gestures With Low Probability of False Positives During Use",19f00a21ce64d05278c5ecee12b6d0c1c2bd4b84,2044339,0,0,33,1398650168,0
Paper,"An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity",519e15dbeab4f1c9fb79c7699f75db6089c6b6bf,190689,0,0,29,1398650168,0
Paper,"Denoising Source Separation",510729a50981a497de66ac7cec570eb08a6b7c79,1950865,0,0,24,1398650168,0
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Paper,"Bounded Kernel-Based Online Learning",2aa2212325162e2d22c8fbb2790fb1b5d30fe275,429447,0,0,38,1398650178,0
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Paper,"Polynomial Identification in the Limit of Substitutable Context-free Languages",8582796ce77f239d88f9d65e026c7bf64f7ce642,150260,0,0,25,1398650179,0
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Paper,"Kernel-Based Learning of Hierarchical Multilabel Classification Models (Special Topic on Machine Learning and Optimization)",94712fb735291e85dc0ba56754845a08d5fff917,193087,0,0,32,1398650179,0
Paper,"Stability Properties of Empirical Risk Minimization over Donsker Classes",164dfc2054da248f85722945ffb0777ddc89eddc,164348,0,0,29,1398650179,0
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Paper,"Structured Prediction, Dual Extragradient and Bregman Projections (Special Topic on Machine Learning and Optimization)",161c197fd72aeb5eb6f06162174759bafacb6463,534290,0,0,36,1398650180,0
Paper,"Large Margin Semi-supervised Learning",d9e261b11c763abf68d852e6b6b824a7cca7b6de,260985,0,0,23,1398650180,0
Paper,"Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm",9938d5a4bcf4e7986b1059cdb293a5beb65d6233,199047,0,0,26,1398650180,0
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Paper,"Learning from Partial Labels",09cf3400cedcc98b277bb073780faa79a0b80474,1913469,0,0,34,1398650180,0
Paper,"Efficient Structure Learning of Bayesian Networks using Constraints",b5d0a272f00e853c185784d22b3cb5f4c604b153,215951,0,0,30,1398650180,0
Paper,"Generalization Bounds for the Area Under the ROC Curve",325a17176cefb598e7776562e69ef3dfc1fd2480,259604,0,0,30,1398650180,0
Paper,"Variational Inference in Nonconjugate Models",de0d3b62fd9c1efd79ddf106dfe1036d602fa012,500052,0,0,22,1398650181,0
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Paper,"Multiple Kernel Learning Algorithms",83f0dc25b1c9175df96fbe168bd153226b4fa473,395805,0,0,26,1398650181,0
Paper,"Multi-Task Learning for Classification with Dirichlet Process Priors",38cf8e6441722f7ca42d2c2c28fe8248e2f55472,250967,0,0,24,1398650181,0
Paper,"Efficient Learning with Partially Observed Attributes",04722103ad77ab5639f019773a828c82183f8f60,187318,0,0,29,1398650181,0
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Paper,"The Learning-Curve Sampling Method Applied to Model-Based Clustering",4438c8dba4c0e08576a6cbe1db3ef9ab04c9f922,193580,0,0,27,1398650181,0
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Paper,"Inner Product Spaces for Bayesian Networks",b328509aa436f2928fd95411b75892fa76c154b7,164607,0,0,26,1398650182,0
Paper,"Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes",f0d7c8957eb8166c781afcb9d4cb800f88dc8d1c,524924,0,0,27,1398650182,0
Paper,"Adaptive Prototype Learning Algorithms: Theoretical and Experimental Studies",044f66e5f830e8af633c12126e17b83e7ca11159,389203,0,0,44,1398650182,0
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Paper,"Uniform Object Generation for Optimizing One-class Classifiers (Kernel Machines Section)",a789d64967f290658051009c71ac12ce521f53f8,188011,0,0,27,1398650182,0
Paper,"Approximate Marginals in Latent Gaussian Models",46b85abadd8047e372ad9d828267c3caf2010015,3570298,0,0,28,1398650182,0
Paper,"Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection",45c959bd128b0d7c8f8bb56a142feace3ded16aa,243076,0,0,26,1398650182,0
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Paper,"Ensemble Pruning Via Semi-definite Programming (Special Topic on Machine Learning and Optimization)",dd46e5dd71f1d030fecbeaefe8f160db8a667c4c,262394,0,0,29,1398650183,0
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Paper,"Incremental Support Vector Learning: Analysis, Implementation and Applications (Special Topic on Machine Learning and Optimization)",c10a3083a19620e20dd28eec55d39763695469c2,259910,0,0,35,1398650183,0
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Paper,"A Generalized Kernel Approach to Dissimilarity-based Classification (Kernel Machines Section)",620871753fb3a80f0177dcc20a193648dbb8197d,451295,0,0,26,1398650184,0
Paper,"Worst-Case Analysis of Selective Sampling for Linear Classification",6337ffb8648d6c0dd01b9d5463f86c66f7b1c96f,184339,0,0,25,1398650184,0
Paper,"Unsupervised Supervised Learning II: Margin-Based Classification Without Labels",abfba3928f07f06af6d2f37f98c56731144689dd,253973,0,0,29,1398650184,0
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Paper,"Stochastic Methods for l1-regularized Loss Minimization",4643cebc79b7e7f77ca33093c614cb171a4dac9c,517561,0,0,27,1398650184,0
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Paper,"Linear Programming Relaxations and Belief Propagation -- An Empirical Study (Special Topic on Machine Learning and Optimization)",adc57fd2617de4ac0da9741eb465bab36f80d757,802836,0,0,47,1398650184,0
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Paper,"Scikit-learn: Machine Learning in Python",5ba4939a00a9b21629a0ad7d376898b768d997a3,42310,25,1,2071,1398650187,0
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Paper,"Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood",43174c736752d532c6beef2db3a51f160f3e20d4,447140,1,0,31,1398650187,0
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Paper,"Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis",34e81945b75f04fc9d761ff03107482eb6fdbda6,487931,0,0,26,1398650188,0
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Paper,"On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines (Kernel Machines Section)",e5e4db66ae2b6757f9a4ddca5ea6793db4c9b267,494387,0,0,29,1398650188,0
Paper,"X-Armed Bandits",82a415bed4fd188b34ea7b50c2b92c5659428a50,835486,0,0,29,1398650188,0
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Paper,"A Stochastic Algorithm for Feature Selection in Pattern Recognition",cdb0f212f1ab2bfc77c8280174c2a32bfc640cf0,438674,0,0,42,1398650202,0
Paper,"On the Consistency of Multiclass Classification Methods (Special Topic on the Conference on Learning Theory 2005)",7858fdf307d9fe94aeaaeaeadfc554988b80a3ce,174763,0,0,27,1398650350,0
Paper,"Learning to Select Features using their Properties",0baade895c487724dc4e1bb3c00ac01436c9f767,137755,2,0,31,1398650693,0
Paper,"A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters",72be7eb406e778d33a811ee00b4c2f16bb298670,281478,0,0,25,1398650693,0
Paper,"HPB: A Model for Handling BN Nodes with High Cardinality Parents",dd8607dea92a1f40c2fbf0ced40717a4101d15ef,306205,0,0,21,1398650693,0
Paper,"Model Selection in Kernel Based Regression using the Influence Function(Special Topic on Model Selection)",0db504995fe64f0dca400a11f146ba007da5bcce,264412,1,0,31,1398650693,0
Paper,"Minimal Nonlinear Distortion Principle for Nonlinear Independent Component Analysis",04b87a940d20054d7b8e8517b07594be39a22451,2913342,0,0,33,1398650693,0
Paper,"Complete Identification Methods for the Causal Hierarchy(Special Topic on Causality)",494f67d57abb4a2b2a1569d0957c8465737e35a2,495052,0,0,30,1398650693,0
Paper,"Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers",903a5ecb011e8d9af24933ebe07cbe50e1f07be2,449201,0,0,33,1398650694,0
Paper,"Gradient Tree Boosting for Training Conditional Random Fields",7199cf9eaca3c451860febeafe48565ea1b35a54,2504832,1,0,34,1398650694,0
Paper,"Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks",07778ccabf041692ac10728350a99310ad4bcce0,252227,0,0,27,1398650694,0
Paper,"Active Learning by Spherical Subdivision",9e38b71ba22a194b1ed0008950d2fef430f7b8e8,560527,0,0,28,1398650694,0
Paper,"Nearly Uniform Validation Improves Compression-Based Error Bounds",6dee60ca650e69104ca00cddcfa37d1bf4f24662,194191,0,0,25,1398650694,0
Paper,"Discriminative Learning of Max-Sum Classifiers",375896cb5a477b789da29b77101d39cbda5ac916,1533844,0,0,27,1398650694,0
Paper,"Multi-class Discriminant Kernel Learning via Convex Programming(Special Topic on Model Selection)",2564fc98f00cee827e978026078e996d71540995,269352,0,0,33,1398650694,0
Paper,"Automatic PCA Dimension Selection for High Dimensional Data and Small Sample Sizes",d5c9aa310bbaa11f7ce67c5293e760e558207019,3716712,0,0,31,1398650694,0
Paper,"Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction",64a22f796592c79060c833e8f4f43b57a58e22df,441498,1,0,39,1398650694,0
Paper,"Ranking Categorical Features Using Generalization Properties",944422ba09b0a06697855b56bdb2cbb8d7d8c399,449954,0,0,29,1398650694,0
Paper,"Visualizing Data using t-SNE",9637de2f50952d9d8f52ac301ef04adef7fc7e4e,254269,0,0,25,1398650694,0
Paper,"Non-Parametric Modeling of Partially Ranked Data",530abae83beab7b0c28ada2b2e0c05c501368e39,390105,0,0,25,1398650694,0
Paper,"Learning Similarity with Operator-valued Large-margin Classifiers",fe7472920bcae4ff936715f160f4d19a91500a45,197461,0,0,24,1398650694,0
Paper,"Trust Region Newton Method for Logistic Regression",6c04924c144aa652a22729992fc90c87029fbd5e,48849,0,0,29,1398650694,0
Paper,"Linear-Time Computation of Similarity Measures for Sequential Data",0b94baebf5c94c37b3aac5ae0b1ea64e45712537,533677,1,0,29,1398650694,0
Paper,"On Relevant Dimensions in Kernel Feature Spaces",774436c940f047c67a4c7266b56706f0a0725f76,102561,0,0,22,1398650694,0
Paper,"Learning Control Knowledge for Forward Search Planning",ca5b3f4c71025c801cf3383bf4ade2e3a09f2f54,203047,0,0,26,1398650694,0
Paper,"On the Size and Recovery of Submatrices of Ones in a Random Binary Matrix",8395af658162b29ab6723ab13822a7ca17d6341b,856271,0,0,26,1398650694,0
Paper,"On the Suitable Domain for SVM Training in Image Coding",81a47ae86153d940580a01017b040da445bc28da,521356,0,0,31,1398650694,0
Paper,"SimpleMKL",9a7b033a7a12876bb4b7b2376d9b28e1518cbe99,843949,0,0,28,1398650695,0
Paper,"Finite-Time Bounds for Fitted Value Iteration",5e3d93e553bb1471433e36e94ad686d07f2b3eca,54732,0,0,27,1398650695,0
Paper,"Learning from Multiple Sources",a91d148d082332f0c0b2231c6a08cc7c2869bf69,510004,0,0,29,1398650695,0
Paper,"Randomized Online PCA Algorithms with Regret Bounds that are Logarithmic in the Dimension",1b397cb54c2bb0378b38b64935ea868a8a109183,306689,1,0,47,1398650695,0
Paper,"Bayesian Inference and Optimal Design for the Sparse Linear Model",cd9896d409b4ff16a1c2bcb1d3c51ec2147787ae,448970,3,0,150,1398650695,0
Paper,"Finding Optimal Bayesian Network Given a Super-Structure",59975e2fe7358266a56dcf58b568c8792749dfaa,356667,0,0,33,1398650695,0
Paper,"A Moment Bound for Multi-hinge Classifiers",ebe3027120419d2ae25dee559946f954b0271be0,4119696,0,0,29,1398650696,0
Paper,"Learning Balls of Strings from Edit Corrections",6e8a90310cafa2984f6d6754ad83a60d5b35e5f2,648887,0,0,31,1398650696,0
Paper,"Value Function Based Reinforcement Learning in Changing Markovian Environments",26d1ed2e2fae81bb304ee38baeb6ac7da155d7ed,382697,0,0,29,1398650696,0
Paper,"An Error Bound Based on a Worst Likely Assignment",b731c15f0c42e454ec96775d8f3a6ddcd6409ff9,163133,0,0,23,1398650696,0
Paper,"Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data",76ea1046a70ebe6bfacd53fee64ed6e22835391d,284292,0,0,29,1398650696,0
Paper,"Probabilistic Characterization of Random Decision Trees",cf742ce887877ed5fd5e5f52526bcbe30c222b00,231522,0,0,33,1398650696,0
Paper,"Learning to Combine Motor Primitives Via Greedy Additive Regression",66a93322d0218625ab3e86c285af5e15bf051c09,1883581,0,0,29,1398650696,0
Paper,"An Information Criterion for Variable Selection in Support Vector Machines(Special Topic on Model Selection)",ce3807fa7cfe63dc402cea83a8f5d900fd7cd458,764429,0,0,35,1398650696,0
Paper,"Support Vector Machinery for Infinite Ensemble Learning",13837aac7d63d8f20681f7e6d6af6d0495b06cc7,153849,0,0,28,1398650697,0
Paper,"Online Learning of Complex Prediction Problems Using Simultaneous Projections",f784e9f849bc0f4142145161b23ec7cf181ef026,490020,0,0,41,1398650697,0
Paper,"Closed Sets for Labeled Data",bc40153090478105e03845c9168eef0fe41de929,546173,0,0,27,1398650697,0
Paper,"Value Function Approximation using Multiple Aggregation for Multiattribute Resource Management",5950e2f9531fcafd052a29e6253fced3d0e29fe6,1338397,0,0,36,1398650697,0
Paper,"Theoretical Advantages of Lenient Learners: An Evolutionary Game Theoretic Perspective",378f768db789e965e18e82e6537655a4bd37fe12,293983,0,0,41,1398650697,0
Paper,"Multiple-Instance Learning of Real-Valued Data",936a92932c01c3f5e9994ae8bd2115f4ccb4adc9,7100,0,0,28,1398650697,0
Paper,"Multi-Agent Reinforcement Learning in Common Interest and Fixed Sum Stochastic Games: An Experimental Study",905a1856ae59c83e46961c39774a98d5e93b6f5d,31044,1,0,32,1398650697,0
Paper,"Aggregation of SVM Classifiers Using Sobolev Spaces",a717b062ca3e26c659a513eb1cf48a2d7e760697,197605,1,0,29,1398650697,0
Paper,"Minimal Kernel Classifiers (Kernel Machines Section)",6c07eeed3d15b409e1e61afb2f35998f787f678e,408146,1,0,45,1398650697,0
Paper,"Mixed Membership Stochastic Blockmodels",2d9fa4c14ce0ea510fcbb35cdf2c1c026596dcaf,140425,1,0,30,1398650697,0
Paper,"Data-dependent margin-based generalization bounds for classification",28a4a9d084522d1bf2ffeb3163e2e771a8f8f34c,298604,1,0,33,1398650697,0
Paper,"Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming",28acd76f57bb2194087ef9a57b13d6a4adb2edb5,275986,1,0,33,1398650697,0
Paper,"Causal Reasoning with Ancestral Graphs(Special Topic on Causality)",b19ab3f5b9d697fabd43259db5dc42948356adc3,728929,1,0,30,1398650697,0
Paper,"Robust Submodular Observation Selection",eaf6521c44b5ca8db8d93c57b324ff4c35b34f32,25343849,1,0,46,1398650697,0
Paper,"Magic Moments for Structured Output Prediction",1c4fbdb2caa3b21d17d20cb8c001e434ceaec178,264130,2,0,31,1398650697,0
Paper,"Search for Additive Nonlinear Time Series Causal Models",bc6a95f3a1ec7d89e52d1e6a4ae661dbaa769985,784415,1,0,34,1398650697,0
Paper,"Learning Precise Timing with LSTM Recurrent Networks",f018ed13be7e4ca2f00b740524c3b36d5196cf55,341852,1,0,46,1398650698,0
Paper,"Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space (Kernel Machines Section)",f31b0ef2048424d3b7b66538d6c4da6f7a3c5db5,253433,2,0,47,1398650698,0
Paper,"A Divisive Information-Theoretic Feature Clustering Algorithm for Text Classification (Kernel Machines Section)",244c23e3a7062823ef49de70eab0eb51f2c3468c,301961,0,0,29,1398650698,0
Paper,"Learning to Construct Fast Signal Processing Implementations",f7ae3b62362210a54498015cbc5f94db1ea2ee4d,283990,1,0,32,1398650699,0
Paper,"Use of the Zero-Norm with Linear Models and Kernel Methods (Kernel Machines Section)",8a36766d87a75f389fa1aa990fa71f0731363b9a,230272,1,0,35,1398650699,0
Paper,"Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning",757245e31fd10a11c8794d3384dbffbdf97fd1e1,2179310,1,0,29,1398650699,0
Paper,"Distributional Word Clusters vs. Words for Text Categorization (Kernel Machines Section)",658627300dd23648f58177f238327eb0832871c1,140095,1,0,34,1398650699,0
Paper,"Online Submodular Minimization",5533d2cdec46d72ab67cc9126cf262b8ac6be883,140144,1,0,27,1398650699,0
Paper,"Structural Learning of Chain Graphs via Decomposition",677280641c8076dda3e04887360a272b2405ab0a,809442,1,0,31,1398650699,0
Paper,"A Tutorial on Conformal Prediction",350b9cfb3613f55812577cf980465694de9cdc74,75336,1,0,33,1398650699,0
Paper,"Stationary Features and Cat Detection",5b025381a593836a068f13e3b07f039dd4e5222f,286029,1,0,28,1398650699,0
Paper,"-MDPs: Learning in Varying Environments",3634303099110d62a04931a61d765db4eafe994d,292579,1,0,36,1398650699,0
Paper,"Graphical Models for Structured Classification, with an Application to Interpreting Images of Protein Subcellular Location Patterns",a84843e9bb71109eff0386b39acb4f70a58b4d04,162578,1,0,33,1398650699,0
Paper,"Rademacher and Gaussian Complexities: Risk Bounds and Structural Results",e2c999aee9996bdd2ec49ee83c38cf51680c4e45,309448,1,0,28,1398650699,0
Paper,"Learning Probabilistic Models of Link Structure",05efe66eb343ac4873beadfff6fc89df0ab83eb4,1989259,1,0,37,1398650699,0
Paper,"DEAP: Evolutionary Algorithms Made Easy",ac9a858e4781b9b8a0acb2d12a4ef291e6509fa9,302542,1,0,60,1398650699,0
Paper,"Stopping Criterion for Boosting-Based Data Reduction Techniques: from Binary to Multiclass Problem",0de748dc086791b5cde497b0dca4e896614a7d69,274080,1,0,36,1398650699,0
Paper,"The Set Covering Machine",d7e14370bebe943dc6a4cef89a742de52a6a28c4,599848,1,0,32,1398650699,0
Paper,"Optimization Techniques for Semi-Supervised Support Vector Machines",239d78675e2c2f64f5d6544ff85b5d206f451a26,48167,1,0,31,1398650700,0
Paper,"On Boosting with Polynomially Bounded Distributions",329dd577181e340c38c987edcf47906ce5700777,285321,1,0,25,1398650700,0
Paper,"JNCC2: The Java Implementation Of Naive Credal Classifier 2(Machine Learning Open Source Software Paper)",abe316b76c07662377614dd1d3deeaf7bbd4abfb,184931,1,0,78,1398650700,0
Paper,"Efficient Algorithms for Decision Tree Cross-validation",f6084af01c3b5f141357da7650a46652b7819e22,1012368,1,0,43,1398650700,0
Paper,"Learning Symbolic Representations of Hybrid Dynamical Systems",f41f4f536d2b60782911e1bfa257078d23465384,1985417,1,0,35,1398650700,0
Paper,"An Introduction to Artificial Prediction Markets for Classification",d410f824cb7401a3b0f341279e0e4947cc5f6105,382028,4,0,101,1398650700,0
Paper,"Rejoinder to Reponses to Evidence Contrary to the Statistical View of Boosting",afff4d2d8d6638904760b72dfd7025a031e7562d,362151,1,0,27,1398650700,0
Paper,"An Improved GLMNET for L1-regularized Logistic Regression",7e2714ef3c9935f7b69f3d340e2c87de325b2cd2,549240,2,0,41,1398650700,0
Paper,"Positive Semidefinite Metric Learning Using Boosting-like Algorithms",4b9d141736b5842aa7e384fc1e83e7acd5f815b1,520965,1,0,29,1398650700,0
Paper,"Kernel Methods for Relation Extraction",7856d9f782cdb73ed80434bc67777063090a0032,1699537,1,0,40,1398650700,0
Paper,"Structured Sparsity and Generalization",ceebd84f2e899b59b7210e16cca72f598dffd293,146956,1,0,30,1398650700,0
Paper,"Learning Monotone DNF from a Teacher that Almost Does Not Answer Membership Queries",5a5ca85b282b3b1847523789aa7e164d9a7d83c6,151732,1,0,32,1398650701,0
Paper,"Efficient Methods for Robust Classification Under Uncertainty in Kernel Matrices",d7a243353eec534de466eeb3f2f40b51d9ae2895,395104,1,0,31,1398650701,0
Paper,"A Case Study on Meta-Generalising: A Gaussian Processes Approach",98cf97e5ff54db7b6d12d0b4f98e7002feddad7e,390476,1,0,35,1398650701,0
Paper,"Nonparametric Guidance of Autoencoder Representations using Label Information",53924a521b9c9d67c89a2da3fa8e8f8f1f1c3937,816490,1,0,30,1398650701,0
Paper,"Ranking a Random Feature for Variable and Feature Selection",c65d372e2a2e00f1ff3608764a18a66708ed5cab,142448,2,0,35,1398650701,0
Paper,"Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions",f5c1c6514a5d6adb80fe84966ee28e5d8294f83d,29619,1,0,32,1398650702,0
Paper,"Optimistic Bayesian Sampling in Contextual-Bandit Problems",0b5365e25cf26e1a7592b634ead614db82ed6031,9990704,2,0,46,1398650702,0
Paper,"NIMFA : A Python Library for Nonnegative Matrix Factorization",636e004329b9da7452feab9e543c7bca2a3af65f,35812,1,0,62,1398650702,0
Paper,"Finite-Sample Analysis of Least-Squares Policy Iteration",a0980d654eec0d65bfd5d0fb15c04141b46eb5c7,290026,1,0,30,1398650702,0
Paper,"Ranking Individuals by Group Comparisons",3540028f12669876e8c36cc9cc7bbd38ed239e22,158945,1,0,33,1398650702,0
Paper,"MedLDA: Maximum Margin Supervised Topic Models",52bb0a07bf9bf6bda93e6178b42bcf3a4e9360a0,1321456,1,0,34,1398650702,0
Paper,"EP-GIG Priors and Applications in Bayesian Sparse Learning",b590650675dc8b49b4562bd82144f5649c2f6b29,1228428,1,0,31,1398650702,0
Paper,"Structured Sparsity via Alternating Direction Methods",076ca91cc7f0bd7bb802b6622a5d46ec49b280ea,629885,1,0,41,1398650703,0
Paper,"An Introduction to Variable and Feature Selection (Kernel Machines Section)",29d693fc13be423a38598a7d000c933185f29c90,763446,1,0,42,1398650703,0
Paper,"Large-Sample Learning of Bayesian Networks is NP-Hard",9a4b9f335fc34e1926309b3c6d81900cefe850ce,342067,2,0,57,1398650703,0
Paper,"Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2",f62507cd6f2f5fd5a248b3f43706d8909be1b27d,267556,1,0,29,1398650703,0
Paper,"Multi-Target Regression with Rule Ensembles",e363d82858aa3aea8a32cad442a29ac8d243b333,328302,1,0,29,1398650703,0
Paper,"Static Prediction Games for Adversarial Learning Problems",cdd39295089b63185ca2fb6009858297a3125d0b,619681,1,0,36,1398650703,0
Paper,"Coupled Clustering: A Method for Detecting Structural Correspondence",31bb1d49aa29c536ff8f4edfbb1ae35fdf766135,111017,2,0,49,1398650703,0
Paper,"The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces (Kernel Machines Section)",309dbeb9e5cd88d8bc7ad02d109dd5674549adea,675842,1,0,38,1398650703,0
Paper,"High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion",85d39ec1436373da7c96212f48c59c7f50acc651,380313,1,0,31,1398650703,0
Paper,"A New Algorithm for Estimating the Effective Dimension-Reduction Subspace",b8da80a6506bb92ee0361848b4106e2de6aec52d,292054,1,0,32,1398650703,0
Paper,"Multi-Instance Learning with Any Hypothesis Class",cd9c87f148a42e541d41ac5356c359309a8049f9,328574,1,0,34,1398650703,0
Paper,"Randomized Variable Elimination",8177336f115e53e3de8a416b97194bfe26e84e6d,386372,1,0,32,1398650703,0
Paper,"The Sample Complexity of Exploration in the Multi-Armed Bandit Problem (Special Topic on Learning Theory)",1e3a97735443c7592be48b78733f2349ee0b403f,174521,1,0,34,1398650704,0
Paper,"The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins",af7c1049149f10db2ade5a6b9d9b6fecdcecb0a1,168449,1,0,37,1398650704,0
Paper,"Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction",923d25dc1171104ed2a862fbdfc218ff56f5aa68,184334,1,0,42,1398650705,0
Paper,"Tracking the Best Linear Predictor",c09e7c3fba6fcc802002d6f9a15fe54d8dee5a94,476946,2,0,57,1398650705,0
Paper,"Linear Regression With Random Projections",eb003a400246f6e364bf3ac0bb44067055988bd4,586305,1,0,39,1398650705,0
Paper,"Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences",9ff938b61ed7b5e3d5ebfe3278ce56bd5cc5a3f2,184052,1,0,62,1398650706,0
Paper,"Feature Selection for Unsupervised Learning",9fe1df0d4a7ded57d447c97318b35e60810e32a2,317561,0,0,31,1398650706,0
Paper,"Overlearning in Marginal Distribution-Based ICA: Analysis and Solutions",deb20678ef8a6bbb391b199cafc48998a39ffc1d,2093826,0,0,33,1398650706,0
Paper,"Learning the Kernel Matrix with Semidefinite Programming",2d9247a0b27d6f1130bdcd8b869830ecd8c6243e,386567,0,0,45,1398650706,0
Paper,"In Defense of One-Vs-All Classification",39551e7bd5f0af1f567a2fef9413938c566f0d3b,73107,0,0,26,1398650706,0
Paper,"Learning Ensembles from Bites: A Scalable and Accurate Approach",76fb996864ee7fd80e65c8f6fe44478bb12bbd71,452372,0,0,36,1398650706,0
Paper,"MLPs (Mono-Layer Polynomials and Multi-Layer Perceptrons) for Nonlinear Modeling",2e520073db4ce011c5a05d5a8b08496d47865f9f,183688,0,0,29,1398650706,0
Paper,"Fast String Kernels using Inexact Matching for Protein Sequences",cd5521215c0dc806e8eba38813d34d8263c5ee3c,142587,0,0,41,1398650706,0
Paper,"Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection",2c623a098b9f668b9501b3606ab5f94034d81396,706101,0,0,46,1398650706,0
Paper,"Feature Discovery in Non-Metric Pairwise Data",fad3010c2d13bf634f90b8c452e96742d6d6bcc7,255361,0,0,29,1398650707,0
Paper,"An Approximate Analytical Approach to Resampling Averages (Kernel Machines Section)",0460c8c1896c195a47791759abe21686cbb59962,3158908,0,0,35,1398650707,0
Paper,"On the Convergence Rate of lp-Norm Multiple Kernel Learning",5cf922c6e8277a4e554a5370c8066469c238cd48,417134,0,0,37,1398650707,0
Paper,"SVMTorch: Support Vector Machines for Large-Scale Regression Problems (Kernel Machines Section)",e56fbff6604eb9544fd702e368479af155406413,307974,0,0,36,1398650707,0
Paper,"A Unified Framework for Model-based Clustering",818f8f9a9d516aeb11ca86f8df6d0a5a8febfd45,338530,0,0,28,1398650707,0
Paper,"Robust Principal Component Analysis with Adaptive Selection for Tuning Parameters",aaf5ad6fca5f36419ba219aa90bd87c800b18054,3296670,0,0,31,1398650707,0
Paper,"Inducing Grammars from Sparse Data Sets: A Survey of Algorithms and Results",c867c760e4256aafb009cc7e5b9d893430c4f6e4,249819,0,0,25,1398650707,0
Paper,"Benefitting from the Variables that Variable Selection Discards",b388afb9c9ce20102130a3ecdddf4e685e49797d,176224,0,0,25,1398650707,0
Paper,"Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs",294776d4fe640bb3e5ad3dfcd2328f2346c3d0e0,1835220,0,0,31,1398650707,0
Paper,"On Inclusion-Driven Learning of Bayesian Networks",fe662fc45e7629b07622f64444a77b710a5729bb,245651,0,0,23,1398650707,0
Paper,"The Minimum Error Minimax Probability Machine",6ef48588ccadd8a66f4fb57556644b46988251ce,196392,0,0,30,1398650708,0
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Paper,"Algorithms for Sparse Linear Classifiers in the Massive Data Setting",a2a44d367d849fd323b3da394f52b1b4bbd38ae9,386096,0,0,39,1398650740,0
Paper,"Sally: A Tool for Embedding Strings in Vector Spaces",acef731ff2e0ed888e7f860cb27c1417866c4f95,413354,0,0,32,1398650741,0
Paper,"Rational Kernels: Theory and Algorithms (Special Topic on Learning Theory)",64aacc7f046be66cfcc98e2c684bc5aa1c97d7ca,322046,0,0,37,1398650741,0
Paper,"A Robust Minimax Approach to Classification",bff8467895bf3ea2712ace7d87efb7a5f85b560b,523763,0,0,38,1398650741,0
Paper,"No Unbiased Estimator of the Variance of K-Fold Cross-Validation",cb8c8a3f743a330e249ef360ba6fa2147f4702ba,214685,0,0,35,1398650741,0
Paper,"Knowledge-Based Kernel Approximation",501e017a78ab24b0063bd041f675af7675689b20,152553,0,0,39,1398650741,0
Paper,"Regularized Discriminant Analysis, Ridge Regression and Beyond",e237f7aaaa9cd93a68b3abe3583c1bb75dcaad3d,332200,0,0,36,1398650741,0
Paper,"Comments on the Complete Characterization of a Family of Solutions to a Generalized Fisher Criterion",5aa0104d675b610e87a5b845a03140ea4657cc0d,439537,0,0,34,1398650741,0
Paper,"Pairwise Support Vector Machines and their Application to Large Scale Problems",d058dbfcc0abbcc7ea60eb1696f2d463f692a241,262888,0,0,35,1398650742,0
Paper,"Lyapunov Design for Safe Reinforcement Learning",622c9de43b3ce42daa46a9622e36d3e5bb2c1a21,1127186,0,0,40,1398650742,0
Paper,"Approximations for Binary Gaussian Process Classification",23b7baf9db5d41de8f1f5f57e25f97c26a2637e6,62856,0,0,34,1398650742,0
Paper,"Minimax Manifold Estimation",5a779dec342b3228270697565c01547007ba131a,2325836,0,0,39,1398650743,0
Paper,"A Neural Probabilistic Language Model",ccdfe60f5bb75ca85473c90d483e3802d53f5d12,242236,1,0,76,1398650743,0
Paper,"Lagrangian Support Vector Machines (Kernel Machines Section)",50df310af8d5acc4986cafae0cd716decb2c0592,280392,0,0,60,1398650743,0
Paper,"Restricted Strong Convexity and Weighted Matrix Completion: Optimal Bounds with Noise",d1c93e3859c2d0f6cc9a7e4ad1a6a04c28f2d5c9,280196,0,0,31,1398650744,0
Paper,"Erratum: SGDQN is Less Careful than Expected",5ae1c9c1b06c251c154efefee93dcb23539d914e,998426,0,0,35,1398650744,0
Paper,"Extensions to Metric-Based Model Selection",93a55ffbc30b6cfefaa4b665e979717e99d4405e,657989,0,0,34,1398650783,0
Paper,"A Kernel Two-Sample Test",234705eaac998d5c04b8e4c8a096c120d66404a0,479876,0,0,40,1398650785,0
Paper,"On the Necessity of Irrelevant Variables",ffa02bdccbfd01ac5ce35c2bfee6210abb4ddd0f,299759,0,0,28,1398650785,0
Paper,"A Local Spectral Method for Graphs: With Applications to Improving Graph Partitions and Exploring Data Graphs Locally",5a0a7c5620fb0a6525dd940cff562869c737a978,968869,0,0,42,1398650785,0
Paper,"On the Equivalence of Linear Dimensionality-Reducing Transformations",709afa4fc8581b99cd6f0991f6f660786f2326a3,336137,0,0,30,1398650787,0
Paper,"Multi-Assignment Clustering for Boolean Data",df72006546f40f6fc8313fdec7dd4e10d4b913c1,604577,0,0,31,1398650787,0
Paper,"Model Averaging for Prediction with Discrete Bayesian Networks",65a8c28e71972ecba5d24fd655b627f1b2423960,251423,1,0,40,1398650789,0
Paper,"A Unified View of Performance Metrics: Translating Threshold Choice into Expected Classification Loss",dc07b26f8acd99dfc7d1763d46079930a1d1b4c5,563388,1,0,44,1398650794,0
Paper,"Statistical Dynamics of On-line Independent Component Analysis",3b1322671d42bb6e951a700fad288137dcaefece,1016257,0,0,39,1398650794,0
Paper,"Latent Dirichlet Allocation",886506949d35110e5cfcf1cd5c5ffb16e0dc904b,388437,2,0,35,1398650794,0
Paper,"Learning Semantic Lexicons from a Part-of-Speech and Semantically Tagged Corpus Using Inductive Logic Programming",dd06605483247bc97dfdf2a688627f9552792557,533074,1,0,57,1398650795,0
Paper,"A Compression Approach to Support Vector Model Selection",88785a8c94e9ffa4fca127ae27f7a5109eb3648b,1358264,1,0,68,1398650796,0
Paper,"Special Issue on the Eighteenth International Conference on Machine Learning (ICML2001)",818692398ce10db04c3c86df13a5732212f65ef9,443405,0,0,33,1398650796,0
Paper,"Continuous Time Bayesian Network Reasoning and Learning Engine",bfda57d28a4b8a0370bcc80bf096f64c08b6c3b6,34130,0,0,45,1398650796,0
Paper,"Finding Recurrent Patterns from Continuous Sign Language Sentences for Automated Extraction of Signs",b37898e31c5ac61100e878ce252025c023470ad5,982838,0,0,38,1398650797,0
Paper,"Introduction to Special Issue on Independent Components Analysis",57337b3b7e9018a8f640c5033bd6c77d238b03e8,259595,0,0,37,1398650797,0
Paper,"Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming",77750329f24c8f314dd436be7b00bd6ec370ff2f,316400,2,0,47,1398650799,0
Paper,"Local and Global Scaling Reduce Hubs in Space",4308fad66470491613d8e4886126fd2482090480,388422,0,0,48,1398650799,0
Paper,"On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation",1fb1a2623f50c709710e610877d665ca1933fe01,759286,0,0,43,1398650800,0
Paper,"Entropy Search for Information-Efficient Global Optimization",b05a6f20298620c47565f3a219372c7365d68fcb,891099,1,0,39,1398650800,0
Paper,"Learnability, Stability and Uniform Convergence",3fa89ca74baca9a6183c78adcea426cc3f4bf3ef,331233,1,0,36,1398650801,0
Paper,"Using Markov Blankets for Causal Structure Learning(Special Topic on Causality)",8e08ddb27ae589a238417b68e587dcb48350055b,506990,0,0,44,1398650801,0
Paper,"Facilitating Score and Causal Inference Trees for Large Observational Studies",0ee5b8cf1a70ba71aba9c80006ac5f2bb93ad498,355273,1,0,39,1398650802,0
Paper,"Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels",78694e1b7d119bf3f90c774a9d64c7397561fb05,231505,3,0,116,1398650802,0
Paper,"Learning Evaluation Functions to Improve Optimization by Local Search",1192c9b53b9b8e9d0770920ed80fbb079ac3996d,568358,1,0,57,1398650805,0
Paper,"Designing Committees of Models through Deliberate Weighting of Data Points",234ab9d3e095f06f64a92ec1116b569e823463e8,237348,0,0,39,1398650806,0
Paper,"Importance Sampling for Continuous Time Bayesian Networks",1667047cab708a174b089171bfaa40245bd7f83b,246974,0,0,49,1398650810,0
Paper,"Shark(Machine Learning Open Source Software Paper)",0b19f22d3cd0e9736fa1c8da332e083432c265e9,503857,1,0,114,1398650810,0
Paper,"PREA: Personalized Recommendation Algorithms Toolkit",20c5722a541aa1ba00308b29f32edddbf8d99f28,290788,0,0,66,1398650817,0
Paper,"Feature Selection via Dependence Maximization",cf7bf7de805b2266ded5349020a94a6670341096,1553412,0,0,58,1398650817,0
Paper,"Lossless Online Bayesian Bagging",3505ee186a69f85a659c187209e896c10ee5af83,383895,0,0,52,1398651002,0
Paper,"Learning Probabilistic Models: An Expected Utility Maximization Approach",932f340dabe2e3416e210b9b62bfdb8bf6d7ffa3,271695,0,0,87,1398651003,0