JMLR Volume 11

• An Efficient Explanation of Individual Classifications using Game Theory

  Erik Štrumbelj, Igor Kononenko (1):1−18, 2010 PDF BibTeX

• Online Learning for Matrix Factorization and Sparse Coding

  Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro (2):19−60, 2010 PDF BibTeX

• Model Selection: Beyond the Bayesian/Frequentist Divide

  Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley (3):61−87, 2010 PDF BibTeX

• On-Line Sequential Bin Packing

  András György, Gábor Lugosi, György Ottucsàk (4):89−109, 2010 PDF BibTeX

• Classification Methods with Reject Option Based on Convex Risk Minimization

  Ming Yuan, Marten Wegkamp (5):111−130, 2010 PDF BibTeX

• An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data

  Yufeng Ding, Jeffrey S. Simonoff (6):131−170, 2010 PDF BibTeX

• Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation

  Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos (7):171−234, 2010 PDF BibTeX

• Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part II: Analysis and Extensions

  Constantin F. Aliferis, Alexander Statnikov, Ioannis Tsamardinos, Subramani Mani, Xenofon D. Koutsoukos (8):235−284, 2010 PDF BibTeX

• Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure

  Kaname Kojima, Eric Perrier, Seiya Imoto, Satoru Miyano (9):285−310, 2010 PDF BibTeX

• Bundle Methods for Regularized Risk Minimization

  Choon Hui Teo, S.V.N. Vishwanthan, Alex J. Smola, Quoc V. Le (10):311−365, 2010 PDF BibTeX

• A Convergent Online Single Time Scale Actor Critic Algorithm

  Dotan Di Castro, Ron Meir (11):367−410, 2010 PDF BibTeX

• Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting

  Philippos Mordohai, Gérard Medioni (12):411−450, 2010 PDF BibTeX

• Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization

  Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, Samuel Kaski (13):451−490, 2010 PDF BibTeX

• Classification Using Geometric Level Sets

  Kush R. Varshney, Alan S. Willsky (14):491−516, 2010 PDF BibTeX

• Generalized Power Method for Sparse Principal Component Analysis

  Michel Journée, Yurii Nesterov, Peter Richtárik, Rodolphe Sepulchre (15):517−553, 2010 PDF BibTeX

• Approximate Tree Kernels

  Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Robert Müller (16):555−580, 2010 PDF BibTeX

• On Finding Predictors for Arbitrary Families of Processes

  Daniil Ryabko (17):581−602, 2010 PDF BibTeX

• A Rotation Test to Verify Latent Structure

  Patrick O. Perry, Art B. Owen (18):603−624, 2010 PDF BibTeX

• Why Does Unsupervised Pre-training Help Deep Learning?

  Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio (19):625−660, 2010 PDF BibTeX

• Error-Correcting Output Codes Library

  Sergio Escalera, Oriol Pujol, Petia Radeva (20):661−664, 2010 codePDF BibTeX

• Second-Order Bilinear Discriminant Analysis

  Christoforos Christoforou, Robert Haralick, Paul Sajda, Lucas C. Parra (21):665−685, 2010 PDF BibTeX

• On the Rate of Convergence of the Bagged Nearest Neighbor Estimate

  Gérard Biau, Frédéric Cérou, Arnaud Guyader (22):687−712, 2010 PDF BibTeX

• A Fast Hybrid Algorithm for Large-Scale l₁-Regularized Logistic Regression

  Jianing Shi, Wotao Yin, Stanley Osher, Paul Sajda (23):713−741, 2010 PDF BibTeX

• PyBrain

  Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber (24):743−746, 2010 PDF BibTeX

• Maximum Relative Margin and Data-Dependent Regularization

  Pannagadatta K. Shivaswamy, Tony Jebara (25):747−788, 2010 PDF BibTeX

• Stability Bounds for Stationary φ-mixing and β-mixing Processes

  Mehryar Mohri, Afshin Rostamizadeh (26):789−814, 2010 PDF BibTeX

• Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models

  Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin (27):815−848, 2010 PDF BibTeX

• A Streaming Parallel Decision Tree Algorithm

  Yael Ben-Haim, Elad Tom-Tov (28):849−872, 2010 PDF BibTeX

• Image Denoising with Kernels Based on Natural Image Relations

  Valero Laparra, Juan Gutiérrez, Gustavo Camps-Valls, Jesús Malo (29):873−903, 2010 PDF BibTeX

• On Learning with Integral Operators

  Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito (30):905−934, 2010 PDF BibTeX

• On Spectral Learning

  Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil (31):935−953, 2010 PDF BibTeX

• Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data

  Gideon S. Mann, Andrew McCallum (32):955−984, 2010 PDF BibTeX

• Kronecker Graphs: An Approach to Modeling Networks

  Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, Zoubin Ghahramani (33):985−1042, 2010 PDF BibTeX

• Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes

  Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright (34):1043−1080, 2010 PDF BibTeX

• Analysis of Multi-stage Convex Relaxation for Sparse Regularization

  Tong Zhang (35):1081−1107, 2010 PDF BibTeX

• Large Scale Online Learning of Image Similarity Through Ranking

  Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio (36):1109−1135, 2010 PDF BibTeX

• Continuous Time Bayesian Network Reasoning and Learning Engine

  Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu (37):1137−1140, 2010 codePDF BibTeX

• SFO: A Toolbox for Submodular Function Optimization

  Andreas Krause (38):1141−1144, 2010 codePDF BibTeX

• A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning

  Jin Yu, S.V.N. Vishwanathan, Simon Günter, Nicol N. Schraudolph (39):1145−1200, 2010 PDF BibTeX

• Graph Kernels

  S.V.N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt (40):1201−1242, 2010 PDF BibTeX

• Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation

  Miki Aoyagi (41):1243−1272, 2010 PDF BibTeX

• Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation

  Vicenç Gómez, Hilbert J. Kappen, Michael Chertkov (42):1273−1296, 2010 PDF BibTeX

• Learning From Crowds

  Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, Linda Moy (43):1297−1322, 2010 PDF BibTeX

• Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels

  Pinar Donmez, Guy Lebanon, Krishnakumar Balasubramanian (44):1323−1351, 2010 PDF BibTeX

• Learning Translation Invariant Kernels for Classification

  Kamaledin Ghiasi-Shirazi, Reza Safabakhsh, Mostafa Shamsi (45):1353−1390, 2010 PDF BibTeX

• Consistent Nonparametric Tests of Independence

  Arthur Gretton, László Györfi (46):1391−1423, 2010 PDF BibTeX

• Characterization, Stability and Convergence of Hierarchical Clustering Methods

  Gunnar Carlsson, Facundo Mémoli (47):1425−1470, 2010 PDF BibTeX

• Training and Testing Low-degree Polynomial Data Mappings via Linear SVM

  Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin (48):1471−1490, 2010 PDF BibTeX

• Quadratic Programming Feature Selection

  Irene Rodriguez-Lujan, Ramon Huerta, Charles Elkan, Carlos Santa Cruz (49):1491−1516, 2010 PDF BibTeX

• Hilbert Space Embeddings and Metrics on Probability Measures

  Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R.G. Lanckriet (50):1517−1561, 2010 PDF BibTeX

• Near-optimal Regret Bounds for Reinforcement Learning

  Thomas Jaksch, Ronald Ortner, Peter Auer (51):1563−1600, 2010 PDF BibTeX

• MOA: Massive Online Analysis

  Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (52):1601−1604, 2010 codePDF BibTeX

• On the Foundations of Noise-free Selective Classification

  Ran El-Yaniv, Yair Wiener (53):1605−1641, 2010 PDF BibTeX

• Introduction to Causal Inference

  Peter Spirtes (54):1643−1662, 2010 PDF BibTeX

• Consensus-Based Distributed Support Vector Machines

  Pedro A. Forero, Alfonso Cano, Georgios B. Giannakis (55):1663−1707, 2010 PDF BibTeX

• Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity

  Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer (56):1709−1731, 2010 PDF BibTeX

• FastInf: An Efficient Approximate Inference Library

  Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan (57):1733−1736, 2010 codePDF BibTeX

• Evolving Static Representations for Task Transfer

  Phillip Verbancsics, Kenneth O. Stanley (58):1737−1769, 2010 PDF BibTeX

• Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing

  Ryo Yoshida, Mike West (59):1771−1798, 2010 PDF BibTeX

• The SHOGUN Machine Learning Toolbox

  Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, Vojt{{\ve}}ch Franc (60):1799−1802, 2010 codePDF BibTeX

• How to Explain Individual Classification Decisions

  David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Müller (61):1803−1831, 2010 PDF BibTeX

• Permutation Tests for Studying Classifier Performance

  Markus Ojala, Gemma C. Garriga (62):1833−1863, 2010 PDF BibTeX

• Sparse Spectrum Gaussian Process Regression

  Miguel Lázaro-Gredilla, Joaquin Quiñnero-Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal (63):1865−1881, 2010 PDF BibTeX

• Fast and Scalable Local Kernel Machines

  Nicola Segata, Enrico Blanzieri (64):1883−1926, 2010 PDF BibTeX

• Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes

  Liva Ralaivola, Marie Szafranski, Guillaume Stempfel (65):1927−1956, 2010 PDF BibTeX

• Practical Approaches to Principal Component Analysis in the Presence of Missing Values

  Alexander Ilin, Tapani Raiko (66):1957−2000, 2010 PDF BibTeX

• Posterior Regularization for Structured Latent Variable Models

  Kuzman Ganchev, João Graça, Jennifer Gillenwater, Ben Taskar (67):2001−2049, 2010 PDF BibTeX

• A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design

  Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom Dhaene, Karel Crombecq (68):2051−2055, 2010 codePDF BibTeX

• Matrix Completion from Noisy Entries

  Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh (69):2057−2078, 2010 PDF BibTeX

• On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

  Gavin C. Cawley, Nicola L. C. Talbot (70):2079−2107, 2010 PDF BibTeX

• Model-based Boosting 2.0

  Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner (71):2109−2113, 2010 codePDF BibTeX

• Importance Sampling for Continuous Time Bayesian Networks

  Yu Fan, Jing Xu, Christian R. Shelton (72):2115−2140, 2010 PDF BibTeX

• Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases

  Guoqiang Yu, Yuanjian Feng, David J. Miller, Jianhua Xuan, Eric P. Hoffman, Robert Clarke, Ben Davidson, Ie-Ming Shih, Yue Wang (73):2141−2167, 2010 PDF BibTeX

• libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models

  Joris M. Mooij (74):2169−2173, 2010 codePDF BibTeX

• Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence

  Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mukherjee (75):2175−2198, 2010 PDF BibTeX

• Regularized Discriminant Analysis, Ridge Regression and Beyond

  Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan (76):2199−2228, 2010 PDF BibTeX

• Erratum: SGDQN is Less Careful than Expected

  Antoine Bordes, Léon Bottou, Patrick Gallinari, Jonathan Chang, S. Alex Smith (77):2229−2240, 2010 PDF BibTeX

• Restricted Eigenvalue Properties for Correlated Gaussian Designs

  Garvesh Raskutti, Martin J. Wainwright, Bin Yu (78):2241−2259, 2010 PDF BibTeX

• High Dimensional Inverse Covariance Matrix Estimation via Linear Programming

  Ming Yuan (79):2261−2286, 2010 PDF BibTeX

• Spectral Regularization Algorithms for Learning Large Incomplete Matrices

  Rahul Mazumder, Trevor Hastie, Robert Tibshirani (80):2287−2322, 2010 PDF BibTeX

• Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers

  Franz Pernkopf, Jeff A. Bilmes (81):2323−2360, 2010 PDF BibTeX

• High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency

  Dapo Omidiran, Martin J. Wainwright (82):2361−2386, 2010 PDF BibTeX

• Composite Binary Losses

  Mark D. Reid, Robert C. Williamson (83):2387−2422, 2010 PDF BibTeX

• Sparse Semi-supervised Learning Using Conjugate Functions

  Shiliang Sun, John Shawe-Taylor (84):2423−2455, 2010 PDF BibTeX

• Rademacher Complexities and Bounding the Excess Risk in Active Learning

  Vladimir Koltchinskii (85):2457−2485, 2010 PDF BibTeX

• Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data

  Miloš Radovanović, Alexandros Nanopoulos, Mirjana Ivanović (86):2487−2531, 2010 PDF BibTeX

• WEKA−Experiences with a Java Open-Source Project

  Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten (87):2533−2541, 2010 PDF BibTeX

• Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization

  Lin Xiao (88):2543−2596, 2010 PDF BibTeX

• Stochastic Composite Likelihood

  Joshua V. Dillon, Guy Lebanon (89):2597−2633, 2010 PDF BibTeX

• Learnability, Stability and Uniform Convergence

  Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan (90):2635−2670, 2010 PDF BibTeX

• Topology Selection in Graphical Models of Autoregressive Processes

  Jitkomut Songsiri, Lieven Vandenberghe (91):2671−2705, 2010 PDF BibTeX

• Using Contextual Representations to Efficiently Learn Context-Free Languages

  Alexander Clark, Rémi Eyraud, Amaury Habrard (92):2707−2744, 2010 PDF BibTeX

• Mean Field Variational Approximation for Continuous-Time Bayesian Networks

  Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman (93):2745−2783, 2010 PDF BibTeX

• Regret Bounds and Minimax Policies under Partial Monitoring

  Jean-Yves Audibert, Sébastien Bubeck (94):2785−2836, 2010 PDF BibTeX

• Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance

  Nguyen Xuan Vinh, Julien Epps, James Bailey (95):2837−2854, 2010 PDF BibTeX

• Expectation Truncation and the Benefits of Preselection In Training Generative Models

  Jörg Lücke, Julian Eggert (96):2855−2900, 2010 PDF BibTeX

• Linear Algorithms for Online Multitask Classification

  Giovanni Cavallanti, Nicoló Cesa-Bianchi, Claudio Gentile (97):2901−2934, 2010 PDF BibTeX

• Tree Decomposition for Large-Scale SVM Problems

  Fu Chang, Chien-Yang Guo, Xiao-Rong Lin, Chi-Jen Lu (98):2935−2972, 2010 PDF BibTeX

• Semi-Supervised Novelty Detection

  Gilles Blanchard, Gyemin Lee, Clayton Scott (99):2973−3009, 2010 PDF BibTeX

• Gaussian Processes for Machine Learning (GPML) Toolbox

  Carl Edward Rasmussen, Hannes Nickisch (100):3011−3015, 2010 codePDF BibTeX

• Covariance in Unsupervised Learning of Probabilistic Grammars

  Shay B. Cohen, Noah A. Smith (101):3017−3051, 2010 PDF BibTeX

• Inducing Tree-Substitution Grammars

  Trevor Cohn, Phil Blunsom, Sharon Goldwater (102):3053−3096, 2010 PDF BibTeX

• Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials

  Rahul Gupta, Sunita Sarawagi, Ajit A. Diwan (103):3097−3135, 2010 PDF BibTeX

• A Generalized Path Integral Control Approach to Reinforcement Learning

  Evangelos Theodorou, Jonas Buchli, Stefan Schaal (104):3137−3181, 2010 PDF BibTeX

• A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification

  Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, Chih-Jen Lin (105):3183−3234, 2010 PDF BibTeX

• Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes

  Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen (106):3235−3268, 2010 PDF BibTeX

• Classification with Incomplete Data Using Dirichlet Process Priors

  Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson (107):3269−3311, 2010 PDF BibTeX

• Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds

  Jacek P. Dmochowski, Paul Sajda, Lucas C. Parra (108):3313−3332, 2010 PDF BibTeX

• Learning Instance-Specific Predictive Models

  Shyam Visweswaran, Gregory F. Cooper (109):3333−3369, 2010 PDF BibTeX

• Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

  Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol (110):3371−3408, 2010 PDF BibTeX

• L_p-Nested Symmetric Distributions

  Fabian Sinz, Matthias Bethge (111):3409−3451, 2010 PDF BibTeX

• Efficient Algorithms for Conditional Independence Inference

  Remco Bouckaert, Raymond Hemmecke, Silvia Lindner, Milan Studený (112):3453−3479, 2010 PDF BibTeX

• An Exponential Model for Infinite Rankings

  Marina Meilă, Le Bao (113):3481−3518, 2010 PDF BibTeX

• Rate Minimaxity of the Lasso and Dantzig Selector for the l_q Loss in l_r Balls

  Fei Ye, Cun-Hui Zhang (114):3519−3540, 2010 PDF BibTeX

• Incremental Sigmoid Belief Networks for Grammar Learning

  James Henderson, Ivan Titov (115):3541−3570, 2010 PDF BibTeX

• Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory

  Sumio Watanabe (116):3571−3594, 2010 PDF BibTeX

• PAC-Bayesian Analysis of Co-clustering and Beyond

  Yevgeny Seldin, Naftali Tishby (117):3595−3646, 2010 PDF BibTeX

• Learning Non-Stationary Dynamic Bayesian Networks

  Joshua W. Robinson, Alexander J. Hartemink (118):3647−3680, 2010 PDF BibTeX