JMLR Volume 11

An Efficient Explanation of Individual Classifications using Game Theory
Erik Ċ trumbelj, Igor Kononenko; 11(Jan):1−18, 2010.
[abs][pdf]

Online Learning for Matrix Factorization and Sparse Coding
Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro; 11(Jan):19−60, 2010.
[abs][pdf]

Model Selection: Beyond the Bayesian/Frequentist Divide
Isabelle Guyon, Amir Saffari, Gideon Dror, Gavin Cawley; 11(Jan):61−87, 2010.
[abs][pdf]

On-Line Sequential Bin Packing
András György, Gábor Lugosi, György Ottucsàk; 11(Jan):89−109, 2010.
[abs][pdf]

Classification Methods with Reject Option Based on Convex Risk Minimization
Ming Yuan, Marten Wegkamp; 11(Jan):111−130, 2010.
[abs][pdf]

An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data
Yufeng Ding, Jeffrey S. Simonoff; 11(Jan):131−170, 2010.
[abs][pdf]

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; 11(Jan):171−234, 2010.
[abs][pdf][erratum]

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; 11(Jan):235−284, 2010.
[abs][pdf]

Optimal Search on Clustered Structural Constraint for Learning Bayesian Network Structure
Kaname Kojima, Eric Perrier, Seiya Imoto, Satoru Miyano; 11(Jan):285−310, 2010.
[abs][pdf]

Bundle Methods for Regularized Risk Minimization
Choon Hui Teo, S.V.N. Vishwanthan, Alex J. Smola, Quoc V. Le; 11(Jan):311−365, 2010.
[abs][pdf]

A Convergent Online Single Time Scale Actor Critic Algorithm
Dotan Di Castro, Ron Meir; 11(Jan):367−410, 2010.
[abs][pdf]

Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
Philippos Mordohai, Gérard Medioni; 11(Jan):411−450, 2010.
[abs][pdf]

Information Retrieval Perspective to Nonlinear Dimensionality Reduction for Data Visualization
Jarkko Venna, Jaakko Peltonen, Kristian Nybo, Helena Aidos, Samuel Kaski; 11(Feb):451−490, 2010.
[abs][pdf]

Classification Using Geometric Level Sets
Kush R. Varshney, Alan S. Willsky; 11(Feb):491−516, 2010.
[abs][pdf]

Generalized Power Method for Sparse Principal Component Analysis
Michel Journée, Yurii Nesterov, Peter Richtárik, Rodolphe Sepulchre; 11(Feb):517−553, 2010.
[abs][pdf]

Approximate Tree Kernels
Konrad Rieck, Tammo Krueger, Ulf Brefeld, Klaus-Robert Müller; 11(Feb):555−580, 2010.
[abs][pdf]

On Finding Predictors for Arbitrary Families of Processes
Daniil Ryabko; 11(Feb):581−602, 2010.
[abs][pdf]

A Rotation Test to Verify Latent Structure
Patrick O. Perry, Art B. Owen; 11(Feb):603−624, 2010.
[abs][pdf]

Why Does Unsupervised Pre-training Help Deep Learning?
Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio; 11(Feb):625−660, 2010.
[abs][pdf]

Error-Correcting Output Codes Library
Sergio Escalera, Oriol Pujol, Petia Radeva; 11(Feb):661−664, 2010.
[abs][pdf]    [code][mloss.org]

Second-Order Bilinear Discriminant Analysis
Christoforos Christoforou, Robert Haralick, Paul Sajda, Lucas C. Parra; 11(Feb):665−685, 2010.
[abs][pdf]

On the Rate of Convergence of the Bagged Nearest Neighbor Estimate
Gérard Biau, Frédéric Cérou, Arnaud Guyader; 11(Feb):687−712, 2010.
[abs][pdf]

A Fast Hybrid Algorithm for Large-Scale l1-Regularized Logistic Regression
Jianing Shi, Wotao Yin, Stanley Osher, Paul Sajda; 11(Feb):713−741, 2010.
[abs][pdf]

PyBrain
Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder, Frank Sehnke, Thomas Rückstieß, Jürgen Schmidhuber; 11(Feb):743−746, 2010.
[abs][pdf]    [code][mloss.org]

Maximum Relative Margin and Data-Dependent Regularization
Pannagadatta K. Shivaswamy, Tony Jebara; 11(Feb):747−788, 2010.
[abs][pdf]

Stability Bounds for Stationary φ-mixing and β-mixing Processes
Mehryar Mohri, Afshin Rostamizadeh; 11(Feb):789−814, 2010.
[abs][pdf]

Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models
Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, Chih-Jen Lin; 11(Feb):815−848, 2010.
[abs][pdf]

A Streaming Parallel Decision Tree Algorithm
Yael Ben-Haim, Elad Tom-Tov; 11(Feb):849−872, 2010.
[abs][pdf]

Image Denoising with Kernels Based on Natural Image Relations
Valero Laparra, Juan Gutiérrez, Gustavo Camps-Valls, Jesús Malo; 11(Feb):873−903, 2010.
[abs][pdf]

On Learning with Integral Operators
Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito; 11(Feb):905−934, 2010.
[abs][pdf]

On Spectral Learning
Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil; 11(Feb):935−953, 2010.
[abs][pdf]

Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data
Gideon S. Mann, Andrew McCallum; 11(Feb):955−984, 2010.
[abs][pdf]

Kronecker Graphs: An Approach to Modeling Networks
Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, Zoubin Ghahramani; 11(Feb):985−1042, 2010.
[abs][pdf]

Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes
Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright; 11(Mar):1043−1080, 2010.
[abs][pdf]

Analysis of Multi-stage Convex Relaxation for Sparse Regularization
Tong Zhang; 11(Mar):1081−1107, 2010.
[abs][pdf]

Large Scale Online Learning of Image Similarity Through Ranking
Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio; 11(Mar):1109−1135, 2010.
[abs][pdf]

Continuous Time Bayesian Network Reasoning and Learning Engine
Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu; 11(Mar):1137−1140, 2010.
[abs][pdf]    [code][mloss.org]

SFO: A Toolbox for Submodular Function Optimization
Andreas Krause; 11(Mar):1141−1144, 2010.
[abs][pdf]    [code][mloss.org]

A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning
Jin Yu, S.V.N. Vishwanathan, Simon Güunter, Nicol N. Schraudolph; 11(Mar):1145−1200, 2010.
[abs][pdf]

Graph Kernels
S.V.N. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt; 11(Apr):1201−1242, 2010.
[abs][pdf][erratum]

Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation
Miki Aoyagi; 11(Apr):1243−1272, 2010.
[abs][pdf]

Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation
Vicenç Gómez, Hilbert J. Kappen, Michael Chertkov; 11(Apr):1273−1296, 2010.
[abs][pdf]

Learning From Crowds
Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, Linda Moy; 11(Apr):1297−1322, 2010.
[abs][pdf]

Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels
Pinar Donmez, Guy Lebanon, Krishnakumar Balasubramanian; 11(Apr):1323−1351, 2010.
[abs][pdf]

Learning Translation Invariant Kernels for Classification
Kamaledin Ghiasi-Shirazi, Reza Safabakhsh, Mostafa Shamsi; 11(Apr):1353−1390, 2010.
[abs][pdf]

Consistent Nonparametric Tests of Independence
Arthur Gretton, László Györfi; 11(Apr):1391−1423, 2010.
[abs][pdf]

Characterization, Stability and Convergence of Hierarchical Clustering Methods
Gunnar Carlsson, Facundo Mémoli; 11(Apr):1425−1470, 2010.
[abs][pdf]

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; 11(Apr):1471−1490, 2010.
[abs][pdf]

Quadratic Programming Feature Selection
Irene Rodriguez-Lujan, Ramon Huerta, Charles Elkan, Carlos Santa Cruz; 11(Apr):1491−1516, 2010.
[abs][pdf]

Hilbert Space Embeddings and Metrics on Probability Measures
Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R.G. Lanckriet; 11(Apr):1517−1561, 2010.
[abs][pdf]

Near-optimal Regret Bounds for Reinforcement Learning
Thomas Jaksch, Ronald Ortner, Peter Auer; 11(Apr):1563−1600, 2010.
[abs][pdf]

MOA: Massive Online Analysis
Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer; 11(May):1601−1604, 2010.
[abs][pdf]    [code][mloss.org]

On the Foundations of Noise-free Selective Classification
Ran El-Yaniv, Yair Wiener; 11(May):1605−1641, 2010.
[abs][pdf]

Introduction to Causal Inference
Peter Spirtes; 11(May):1643−1662, 2010.
[abs][pdf]

Consensus-Based Distributed Support Vector Machines
Pedro A. Forero, Alfonso Cano, Georgios B. Giannakis; 11(May):1663−1707, 2010.
[abs][pdf]

Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer; 11(May):1709−1731, 2010.
[abs][pdf]

FastInf: An Efficient Approximate Inference Library
Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan; 11(May):1733−1736, 2010.
[abs][pdf]    [code][mloss.org]

Evolving Static Representations for Task Transfer
Phillip Verbancsics, Kenneth O. Stanley; 11(May):1737−1769, 2010.
[abs][pdf]

Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing
Ryo Yoshida, Mike West; 11(May):1771−1798, 2010.
[abs][pdf]

The SHOGUN Machine Learning Toolbox
Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, Vojtěch Franc; 11(Jun):1799−1802, 2010.
[abs][pdf]    [code][mloss.org]

How to Explain Individual Classification Decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Müller; 11(Jun):1803−1831, 2010.
[abs][pdf]

Permutation Tests for Studying Classifier Performance
Markus Ojala, Gemma C. Garriga; 11(Jun):1833−1863, 2010.
[abs][pdf]

Sparse Spectrum Gaussian Process Regression
Miguel Lázaro-Gredilla, Joaquin Quiñonero-Candela, Carl Edward Rasmussen, Aníbal R. Figueiras-Vidal; 11(Jun):1865−1881, 2010.
[abs][pdf]

Fast and Scalable Local Kernel Machines
Nicola Segata, Enrico Blanzieri; 11(Jun):1883−1926, 2010.
[abs][pdf]

Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes
Liva Ralaivola, Marie Szafranski, Guillaume Stempfel; 11(Jul):1927−1956, 2010.
[abs][pdf]

Practical Approaches to Principal Component Analysis in the Presence of Missing Values
Alexander Ilin, Tapani Raiko; 11(Jul):1957−2000, 2010.
[abs][pdf]

Posterior Regularization for Structured Latent Variable Models
Kuzman Ganchev, João Graça, Jennifer Gillenwater, Ben Taskar; 11(Jul):2001−2049, 2010.
[abs][pdf]

A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom Dhaene, Karel Crombecq; 11(Jul):2051−2055, 2010.
[abs][pdf]    [code][mloss.org]

Matrix Completion from Noisy Entries
Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh; 11(Jul):2057−2078, 2010.
[abs][pdf]

On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
Gavin C. Cawley, Nicola L. C. Talbot; 11(Jul):2079−2107, 2010.
[abs][pdf]

Model-based Boosting 2.0
Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner; 11(Aug):2109−2113, 2010.
[abs][pdf]    [code][mloss.org]

Importance Sampling for Continuous Time Bayesian Networks
Yu Fan, Jing Xu, Christian R. Shelton; 11(Aug):2115−2140, 2010.
[abs][pdf]

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; 11(Aug):2141−2167, 2010.
[abs][pdf]

libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
Joris M. Mooij; 11(Aug):2169−2173, 2010.
[abs][pdf]    [code][mloss.org]

Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence
Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mukherjee; 11(Aug):2175−2198, 2010.
[abs][pdf]

Regularized Discriminant Analysis, Ridge Regression and Beyond
Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan; 11(Aug):2199−2228, 2010.
[abs][pdf]

Erratum: SGDQN is Less Careful than Expected
Antoine Bordes, Léon Bottou, Patrick Gallinari, Jonathan Chang, S. Alex Smith; 11(Aug):2229−2240, 2010.
[abs][pdf]

Restricted Eigenvalue Properties for Correlated Gaussian Designs
Garvesh Raskutti, Martin J. Wainwright, Bin Yu; 11(Aug):2241−2259, 2010.
[abs][pdf]

High Dimensional Inverse Covariance Matrix Estimation via Linear Programming
Ming Yuan; 11(Aug):2261−2286, 2010.
[abs][pdf]

Spectral Regularization Algorithms for Learning Large Incomplete Matrices
Rahul Mazumder, Trevor Hastie, Robert Tibshirani; 11(Aug):2287−2322, 2010.
[abs][pdf]

Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers
Franz Pernkopf, Jeff A. Bilmes; 11(Aug):2323−2360, 2010.
[abs][pdf]

High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency
Dapo Omidiran, Martin J. Wainwright; 11(Aug):2361−2386, 2010.
[abs][pdf]

Composite Binary Losses
Mark D. Reid, Robert C. Williamson; 11(Sep):2387−2422, 2010.
[abs][pdf]

Sparse Semi-supervised Learning Using Conjugate Functions
Shiliang Sun, John Shawe-Taylor; 11(Sep):2423−2455, 2010.
[abs][pdf]

Rademacher Complexities and Bounding the Excess Risk in Active Learning
Vladimir Koltchinskii; 11(Sep):2457−2485, 2010.
[abs][pdf]

Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data
Miloš Radovanović, Alexandros Nanopoulos, Mirjana Ivanović; 11(Sep):2487−2531, 2010.
[abs][pdf]

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; 11(Sep):2533−2541, 2010.
[abs][pdf]

Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization
Lin Xiao; 11(Oct):2543−2596, 2010.
[abs][pdf]

Stochastic Composite Likelihood
Joshua V. Dillon, Guy Lebanon; 11(Oct):2597−2633, 2010.
[abs][pdf]

Learnability, Stability and Uniform Convergence
Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan; 11(Oct):2635−2670, 2010.
[abs][pdf]

Topology Selection in Graphical Models of Autoregressive Processes
Jitkomut Songsiri, Lieven Vandenberghe; 11(Oct):2671−2705, 2010.
[abs][pdf]

Using Contextual Representations to Efficiently Learn Context-Free Languages
Alexander Clark, Rémi Eyraud, Amaury Habrard; 11(Oct):2707−2744, 2010.
[abs][pdf]

Mean Field Variational Approximation for Continuous-Time Bayesian Networks
Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman; 11(Oct):2745−2783, 2010.
[abs][pdf]

Regret Bounds and Minimax Policies under Partial Monitoring
Jean-Yves Audibert, Sébastien Bubeck; 11(Oct):2785−2836, 2010.
[abs][pdf]

Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance
Nguyen Xuan Vinh, Julien Epps, James Bailey; 11(Oct):2837−2854, 2010.
[abs][pdf]

Expectation Truncation and the Benefits of Preselection In Training Generative Models
Jörg Lücke, Julian Eggert; 11(Oct):2855−2900, 2010.
[abs][pdf]

Linear Algorithms for Online Multitask Classification
Giovanni Cavallanti, Nicolò Cesa-Bianchi, Claudio Gentile; 11(Oct):2901−2934, 2010.
[abs][pdf]

Tree Decomposition for Large-Scale SVM Problems
Fu Chang, Chien-Yang Guo, Xiao-Rong Lin, Chi-Jen Lu; 11(Oct):2935−2972, 2010.
[abs][pdf]

Semi-Supervised Novelty Detection
Gilles Blanchard, Gyemin Lee, Clayton Scott; 11(Nov):2973−3009, 2010.
[abs][pdf]

Gaussian Processes for Machine Learning (GPML) Toolbox
Carl Edward Rasmussen, Hannes Nickisch; 11(Nov):3011−3015, 2010.
[abs][pdf]    [code][mloss.org]

Covariance in Unsupervised Learning of Probabilistic Grammars
Shay B. Cohen, Noah A. Smith; 11(Nov):3017−3051, 2010.
[abs][pdf]

Inducing Tree-Substitution Grammars
Trevor Cohn, Phil Blunsom, Sharon Goldwater; 11(Nov):3053−3096, 2010.
[abs][pdf]

Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials
Rahul Gupta, Sunita Sarawagi, Ajit A. Diwan; 11(Nov):3097−3135, 2010.
[abs][pdf]

A Generalized Path Integral Control Approach to Reinforcement Learning
Evangelos Theodorou, Jonas Buchli, Stefan Schaal; 11(Nov):3137−3181, 2010.
[abs][pdf][erratum]

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; 11(Nov):3183−3234, 2010.
[abs][pdf]

Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes
Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen; 11(Nov):3235−3268, 2010.
[abs][pdf]

Classification with Incomplete Data Using Dirichlet Process Priors
Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson; 11(Dec):3269−3311, 2010.
[abs][pdf]

Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds
Jacek P. Dmochowski, Paul Sajda, Lucas C. Parra; 11(Dec):3313−3332, 2010.
[abs][pdf]

Learning Instance-Specific Predictive Models
Shyam Visweswaran, Gregory F. Cooper; 11(Dec):3333−3369, 2010.
[abs][pdf]

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; 11(Dec):3371−3408, 2010.
[abs][pdf]

Lp-Nested Symmetric Distributions
Fabian Sinz, Matthias Bethge; 11(Dec):3409−3451, 2010.
[abs][pdf]

Efficient Algorithms for Conditional Independence Inference
Remco Bouckaert, Raymond Hemmecke, Silvia Lindner, Milan Studený; 11(Dec):3453−3479, 2010.
[abs][pdf]

An Exponential Model for Infinite Rankings
Marina Meilă, Le Bao; 11(Dec):3481−3518, 2010.
[abs][pdf]

Rate Minimaxity of the Lasso and Dantzig Selector for the lq Loss in lr Balls
Fei Ye, Cun-Hui Zhang; 11(Dec):3519−3540, 2010.
[abs][pdf]

Incremental Sigmoid Belief Networks for Grammar Learning
James Henderson, Ivan Titov; 11(Dec):3541−3570, 2010.
[abs][pdf]

Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory
Sumio Watanabe; 11(Dec):3571−3594, 2010.
[abs][pdf]

PAC-Bayesian Analysis of Co-clustering and Beyond
Yevgeny Seldin, Naftali Tishby; 11(Dec):3595−3646, 2010.
[abs][pdf]

Learning Non-Stationary Dynamic Bayesian Networks
Joshua W. Robinson, Alexander J. Hartemink; 11(Dec):3647−3680, 2010.
[abs][pdf]




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