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ORCIDJournal of Machine Learning Research, Volume 11
Volume 11, 2010
- Erik Strumbelj, Igor Kononenko:
An Efficient Explanation of Individual Classifications using Game Theory. 1-18 - Julien Mairal, Francis R. Bach, Jean Ponce, Guillermo Sapiro:
Online Learning for Matrix Factorization and Sparse Coding. 19-60 - Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin C. Cawley:
Model Selection: Beyond the Bayesian/Frequentist Divide. 61-87 - András György, Gábor Lugosi, György Ottucsák:
On-Line Sequential Bin Packing. 89-109 - Ming Yuan, Marten H. Wegkamp:
Classification Methods with Reject Option Based on Convex Risk Minimization. 111-130 - Yufeng Ding, Jeffrey S. Simonoff:
An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data. 131-170 - Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos:
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation. 171-234 - Constantin F. Aliferis, Alexander R. Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos:
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions. 235-284 - Kaname Kojima, Eric Perrier, Seiya Imoto, Satoru Miyano:
Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure. 285-310 - Choon Hui Teo, S. V. N. Vishwanathan, Alexander J. Smola, Quoc V. Le:
Bundle Methods for Regularized Risk Minimization. 311-365 - Dotan Di Castro, Ron Meir:
A Convergent Online Single Time Scale Actor Critic Algorithm. 367-410 - Philippos Mordohai, Gérard G. Medioni:
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting. 411-450 - Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, Samuel Kaski:
Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization. 451-490 - Kush R. Varshney, Alan S. Willsky:
Classification Using Geometric Level Sets. 491-516 - Michel Journée, Yurii E. Nesterov, Peter Richtárik, Rodolphe Sepulchre:
Generalized Power Method for Sparse Principal Component Analysis. 517-553 - Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Robert Müller:
Approximate Tree Kernels. 555-580 - Daniil Ryabko:
On Finding Predictors for Arbitrary Families of Processes. 581-602 - Patrick O. Perry, Art B. Owen:
A Rotation Test to Verify Latent Structure. 603-624 - Dumitru Erhan, Yoshua Bengio, Aaron C. Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio:
Why Does Unsupervised Pre-training Help Deep Learning? 625-660 - Sergio Escalera, Oriol Pujol, Petia Radeva:
Error-Correcting Ouput Codes Library. 661-664 - Christoforos Christoforou, Robert M. Haralick, Paul Sajda, Lucas C. Parra:
Second-Order Bilinear Discriminant Analysis. 665-685 - Gérard Biau, Frédéric Cérou, Arnaud Guyader:
On the Rate of Convergence of the Bagged Nearest Neighbor Estimate. 687-712 - Jianing Shi, Wotao Yin, Stanley J. Osher, Paul Sajda:
A Fast Hybrid Algorithm for Large-Scale l1-Regularized Logistic Regression. 713-741 - Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber:
PyBrain. 743-746 - Pannagadatta K. Shivaswamy, Tony Jebara:
Maximum Relative Margin and Data-Dependent Regularization. 747-788 - Mehryar Mohri, Afshin Rostamizadeh:
Stability Bounds for Stationary phi-mixing and beta-mixing Processes. 789-814 - Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin:
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models. 815-848 - Yael Ben-Haim, Elad Tom-Tov:
A Streaming Parallel Decision Tree Algorithm. 849-872 - Valero Laparra, Jaime Gutierrez, Gustavo Camps-Valls, Jesús Malo:
Image Denoising with Kernels Based on Natural Image Relations. 873-903 - Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito:
On Learning with Integral Operators. 905-934 - Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil:
On Spectral Learning. 935-953 - Gideon S. Mann, Andrew McCallum:
Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data. 955-984 - Jure Leskovec, Deepayan Chakrabarti, Jon M. Kleinberg, Christos Faloutsos
, Zoubin Ghahramani:
Kronecker Graphs: An Approach to Modeling Networks. 985-1042 - Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright:
Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes. 1043-1080 - Tong Zhang:
Analysis of Multi-stage Convex Relaxation for Sparse Regularization. 1081-1107 - Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio:
Large Scale Online Learning of Image Similarity Through Ranking. 1109-1135 - Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu:
Continuous Time Bayesian Network Reasoning and Learning Engine. 1137-1140 - Andreas Krause:
SFO: A Toolbox for Submodular Function Optimization. 1141-1144 - Jin Yu, S. V. N. Vishwanathan, Simon Günter, Nicol N. Schraudolph:
A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning. 1145-1200 - S. V. N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt:
Graph Kernels. 1201-1242 - Miki Aoyagi:
Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation. 1243-1272 - Vicenç Gómez, Hilbert J. Kappen, Michael Chertkov:
Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation. 1273-1296 - Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, Linda Moy:
Learning From Crowds. 1297-1322 - Pinar Donmez, Guy Lebanon, Krishnakumar Balasubramanian:
Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels. 1323-1351 - Sayed Kamaledin Ghiasi Shirazi, Reza Safabakhsh, Mostafa Shamsi:
Learning Translation Invariant Kernels for Classification. 1353-1390 - Arthur Gretton, László Györfi:
Consistent Nonparametric Tests of Independence. 1391-1423 - Gunnar E. Carlsson, Facundo Mémoli:
Characterization, Stability and Convergence of Hierarchical Clustering Methods. 1425-1470 - Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin:
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM. 1471-1490 - Irene Rodríguez-Luján, Ramón Huerta, Charles Elkan, Carlos Santa Cruz:
Quadratic Programming Feature Selection. 1491-1516 - Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R. G. Lanckriet:
Hilbert Space Embeddings and Metrics on Probability Measures. 1517-1561 - Thomas Jaksch, Ronald Ortner, Peter Auer:
Near-optimal Regret Bounds for Reinforcement Learning. 1563-1600 - Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer:
MOA: Massive Online Analysis. 1601-1604 - Ran El-Yaniv, Yair Wiener:
On the Foundations of Noise-free Selective Classification. 1605-1641 - Peter Spirtes:
Introduction to Causal Inference. 1643-1662 - Pedro A. Forero, Alfonso Cano, Georgios B. Giannakis:
Consensus-Based Distributed Support Vector Machines. 1663-1707 - Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer:
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. 1709-1731 - Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan:
FastInf: An Efficient Approximate Inference Library. 1733-1736 - Phillip Verbancsics, Kenneth O. Stanley:
Evolving Static Representations for Task Transfer. 1737-1769 - Ryo Yoshida, Mike West:
Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing. 1771-1798 - Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio De Bona, Alexander Binder, Christian Gehl, Vojtech Franc:
The SHOGUN Machine Learning Toolbox. 1799-1802 - David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Müller:
How to Explain Individual Classification Decisions. 1803-1831 - Markus Ojala, Gemma C. Garriga:
Permutation Tests for Studying Classifier Performance. 1833-1863 - Miguel Lázaro-Gredilla, Joaquin Quiñonero Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal:
Sparse Spectrum Gaussian Process Regression. 1865-1881 - Nicola Segata, Enrico Blanzieri:
Fast and Scalable Local Kernel Machines. 1883-1926 - Liva Ralaivola, Marie Szafranski, Guillaume Stempfel:
Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes. 1927-1956 - Alexander Ilin, Tapani Raiko:
Practical Approaches to Principal Component Analysis in the Presence of Missing Values. 1957-2000 - Kuzman Ganchev, João Graça, Jennifer Gillenwater, Ben Taskar:
Posterior Regularization for Structured Latent Variable Models. 2001-2049 - Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom Dhaene, Karel Crombecq:
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design. 2051-2055 - Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh:
Matrix Completion from Noisy Entries. 2057-2078 - Gavin C. Cawley, Nicola L. C. Talbot:
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation. 2079-2107 - Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner:
Model-based Boosting 2.0. 2109-2113 - Yu Fan, Jing Xu, Christian R. Shelton:
Importance Sampling for Continuous Time Bayesian Networks. 2115-2140 - Guoqiang Yu, Yuanjian Feng, David J. Miller, Jianhua Xuan, Eric P. Hoffman, Robert Clarke, Ben Davidson, Ie-Ming Shih, Yue Joseph Wang:
Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases. 2141-2167 - Joris M. Mooij:
libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models. 2169-2173 - Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mukherjee:
Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence. 2175-2198 - Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan:
Regularized Discriminant Analysis, Ridge Regression and Beyond. 2199-2228 - Antoine Bordes, Léon Bottou, Patrick Gallinari, Jonathan D. Chang, S. Alex Smith:
Erratum: SGDQN is Less Careful than Expected. 2229-2240 - Garvesh Raskutti, Martin J. Wainwright, Bin Yu:
Restricted Eigenvalue Properties for Correlated Gaussian Designs. 2241-2259 - Ming Yuan:
High Dimensional Inverse Covariance Matrix Estimation via Linear Programming. 2261-2286 - Rahul Mazumder, Trevor Hastie, Robert Tibshirani:
Spectral Regularization Algorithms for Learning Large Incomplete Matrices. 2287-2322 - Franz Pernkopf, Jeff A. Bilmes:
Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers. 2323-2360 - Dapo Omidiran, Martin J. Wainwright:
High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency. 2361-2386 - Mark D. Reid, Robert C. Williamson:
Composite Binary Losses. 2387-2422 - Shiliang Sun, John Shawe-Taylor:
Sparse Semi-supervised Learning Using Conjugate Functions. 2423-2455 - Vladimir Koltchinskii:
Rademacher Complexities and Bounding the Excess Risk in Active Learning. 2457-2485 - Milos Radovanovic, Alexandros Nanopoulos, Mirjana Ivanovic:
Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data. 2487-2531 - Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten:
WEKA - Experiences with a Java Open-Source Project. 2533-2541 - Lin Xiao:
Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization. 2543-2596 - Joshua V. Dillon, Guy Lebanon:
Stochastic Composite Likelihood. 2597-2633 - Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan:
Learnability, Stability and Uniform Convergence. 2635-2670 - Jitkomut Songsiri, Lieven Vandenberghe:
Topology Selection in Graphical Models of Autoregressive Processes. 2671-2705 - Alexander Clark, Rémi Eyraud, Amaury Habrard:
Using Contextual Representations to Efficiently Learn Context-Free Languages. 2707-2744 - Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman:
Mean Field Variational Approximation for Continuous-Time Bayesian Networks. 2745-2783 - Jean-Yves Audibert, Sébastien Bubeck:
Regret Bounds and Minimax Policies under Partial Monitoring. 2785-2836 - Xuan Vinh Nguyen, Julien Epps, James Bailey:
Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance. 2837-2854 - Jörg Lücke, Julian Eggert:
Expectation Truncation and the Benefits of Preselection In Training Generative Models. 2855-2900 - Giovanni Cavallanti, Nicolò Cesa-Bianchi, Claudio Gentile:
Linear Algorithms for Online Multitask Classification. 2901-2934 - Fu Chang, Chien-Yang Guo, Xiao-Rong Lin, Chi-Jen Lu:
Tree Decomposition for Large-Scale SVM Problems. 2935-2972 - Gilles Blanchard, Gyemin Lee, Clayton Scott:
Semi-Supervised Novelty Detection. 2973-3009 - Carl Edward Rasmussen, Hannes Nickisch:
Gaussian Processes for Machine Learning (GPML) Toolbox. 3011-3015 - Shay B. Cohen, Noah A. Smith:
Covariance in Unsupervised Learning of Probabilistic Grammars. 3017-3051 - Trevor Cohn, Phil Blunsom, Sharon Goldwater:
Inducing Tree-Substitution Grammars. 3053-3096 - Rahul Gupta, Sunita Sarawagi, Ajit A. Diwan:
Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials. 3097-3135 - Evangelos A. Theodorou, Jonas Buchli, Stefan Schaal:
A Generalized Path Integral Control Approach to Reinforcement Learning. 3137-3181 - Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin:
A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification. 3183-3234 - Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen:
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes. 3235-3268 - Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson:
Classification with Incomplete Data Using Dirichlet Process Priors. 3269-3311 - Jacek P. Dmochowski, Paul Sajda, Lucas C. Parra:
Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds. 3313-3332 - Shyam Visweswaran, Gregory F. Cooper:
Learning Instance-Specific Predictive Models. 3333-3369 - Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol:
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. 3371-3408 - Fabian H. Sinz, Matthias Bethge:
Lp-Nested Symmetric Distributions. 3409-3451 - Remco R. Bouckaert, Raymond Hemmecke, Silvia Lindner, Milan Studený:
Efficient Algorithms for Conditional Independence Inference. 3453-3479 - Marina Meila, Le Bao:
An Exponential Model for Infinite Rankings. 3481-3518 - Fei Ye, Cun-Hui Zhang:
Rate Minimaxity of the Lasso and Dantzig Selector for the lq Loss in lr Balls. 3519-3540 - James Henderson, Ivan Titov:
Incremental Sigmoid Belief Networks for Grammar Learning. 3541-3570 - Sumio Watanabe:
Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory. 3571-3594 - Yevgeny Seldin, Naftali Tishby:
PAC-Bayesian Analysis of Co-clustering and Beyond. 3595-3646 - Joshua W. Robinson, Alexander J. Hartemink:
Learning Non-Stationary Dynamic Bayesian Networks. 3647-3680
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