JMLR Volume 12

Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation
Yizhao Ni, Craig Saunders, Sandor Szedmak, Mahesan Niranjan; 12(Jan):1−30, 2011.
[abs][pdf]

Improved Moves for Truncated Convex Models
M. Pawan Kumar, Olga Veksler, Philip H.S. Torr; 12(Jan):31−67, 2011.
[abs][pdf]

CARP: Software for Fishing Out Good Clustering Algorithms
Volodymyr Melnykov, Ranjan Maitra; 12(Jan):69−73, 2011.
[abs][pdf]    [code][mloss.org]

Multitask Sparsity via Maximum Entropy Discrimination
Tony Jebara; 12(Jan):75−110, 2011.
[abs][pdf]

Bayesian Generalized Kernel Mixed Models
Zhihua Zhang, Guang Dai, Michael I. Jordan; 12(Jan):111−139, 2011.
[abs][pdf]

Training SVMs Without Offset
Ingo Steinwart, Don Hush, Clint Scovel; 12(Jan):141−202, 2011.
[abs][pdf]

Logistic Stick-Breaking Process
Lu Ren, Lan Du, Lawrence Carin, David Dunson; 12(Jan):203−239, 2011.
[abs][pdf]

Online Learning in Case of Unbounded Losses Using Follow the Perturbed Leader Algorithm
Vladimir V. V'yugin; 12(Jan):241−266, 2011.
[abs][pdf]

A Bayesian Approximation Method for Online Ranking
Ruby C. Weng, Chih-Jen Lin; 12(Jan):267−300, 2011.
[abs][pdf]

Cumulative Distribution Networks and the Derivative-sum-product Algorithm: Models and Inference for Cumulative Distribution Functions on Graphs
Jim C. Huang, Brendan J. Frey; 12(Jan):301−348, 2011.
[abs][pdf]

Models of Cooperative Teaching and Learning
Sandra Zilles, Steffen Lange, Robert Holte, Martin Zinkevich; 12(Feb):349−384, 2011.
[abs][pdf]

Operator Norm Convergence of Spectral Clustering on Level Sets
Bruno Pelletier, Pierre Pudlo; 12(Feb):385−416, 2011.
[abs][pdf]

Approximate Marginals in Latent Gaussian Models
Botond Cseke, Tom Heskes; 12(Feb):417−454, 2011.
[abs][pdf]

Posterior Sparsity in Unsupervised Dependency Parsing
Jennifer Gillenwater, Kuzman Ganchev, João Graça, Fernando Pereira, Ben Taskar; 12(Feb):455−490, 2011.
[abs][pdf]

Learning Multi-modal Similarity
Brian McFee, Gert Lanckriet; 12(Feb):491−523, 2011.
[abs][pdf]

Minimum Description Length Penalization for Group and Multi-Task Sparse Learning
Paramveer S. Dhillon, Dean Foster, Lyle H. Ungar; 12(Feb):525−564, 2011.
[abs][pdf]

Variable Sparsity Kernel Learning
Jonathan Aflalo, Aharon Ben-Tal, Chiranjib Bhattacharyya, Jagarlapudi Saketha Nath, Sankaran Raman; 12(Feb):565−592, 2011.
[abs][pdf]

Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach
Gilles Meyer, Silvère Bonnabel, Rodolphe Sepulchre; 12(Feb):593−625, 2011.
[abs][pdf]

Parameter Screening and Optimisation for ILP using Designed Experiments
Ashwin Srinivasan, Ganesh Ramakrishnan; 12(Feb):627−662, 2011.
[abs][pdf]

Efficient Structure Learning of Bayesian Networks using Constraints
Cassio P. de Campos, Qiang Ji; 12(Mar):663−689, 2011.
[abs][pdf]

Inverse Reinforcement Learning in Partially Observable Environments
Jaedeug Choi, Kee-Eung Kim; 12(Mar):691−730, 2011.
[abs][pdf]

Information, Divergence and Risk for Binary Experiments
Mark D. Reid, Robert C. Williamson; 12(Mar):731−817, 2011.
[abs][pdf]

Learning Transformation Models for Ranking and Survival Analysis
Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel; 12(Mar):819−862, 2011.
[abs][pdf]

Sparse Linear Identifiable Multivariate Modeling
Ricardo Henao, Ole Winther; 12(Mar):863−905, 2011.
[abs][pdf]

Forest Density Estimation
Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John Lafferty, Larry Wasserman; 12(Mar):907−951, 2011.
[abs][pdf]

lp-Norm Multiple Kernel Learning
Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Alexander Zien; 12(Mar):953−997, 2011.
[abs][pdf]

Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data
Zeeshan Syed, John Guttag; 12(Mar):999−1024, 2011.
[abs][pdf]

Two Distributed-State Models For Generating High-Dimensional Time Series
Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis; 12(Mar):1025−1068, 2011.
[abs][pdf]

Differentially Private Empirical Risk Minimization
Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate; 12(Mar):1069−1109, 2011.
[abs][pdf]

Anechoic Blind Source Separation Using Wigner Marginals
Lars Omlor, Martin A. Giese; 12(Mar):1111−1148, 2011.
[abs][pdf]

Laplacian Support Vector Machines Trained in the Primal
Stefano Melacci, Mikhail Belkin; 12(Mar):1149−1184, 2011.
[abs][pdf]

The Indian Buffet Process: An Introduction and Review
Thomas L. Griffiths, Zoubin Ghahramani; 12(Apr):1185−1224, 2011.
[abs][pdf]

DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen; 12(Apr):1225−1248, 2011.
[abs][pdf]

Locally Defined Principal Curves and Surfaces
Umut Ozertem, Deniz Erdogmus; 12(Apr):1249−1286, 2011.
[abs][pdf]

Better Algorithms for Benign Bandits
Elad Hazan, Satyen Kale; 12(Apr):1287−1311, 2011.
[abs][pdf]

A Family of Simple Non-Parametric Kernel Learning Algorithms
Jinfeng Zhuang, Ivor W. Tsang, Steven C.H. Hoi; 12(Apr):1313−1347, 2011.
[abs][pdf]

Faster Algorithms for Max-Product Message-Passing
Julian J. McAuley, Tibério S. Caetano; 12(Apr):1349−1388, 2011.
[abs][pdf]

Clustering Algorithms for Chains
Antti Ukkonen; 12(Apr):1389−1423, 2011.
[abs][pdf]

Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning
Dorota Głowacka, John Shawe-Taylor, Alex Clark, Colin de la Higuera, Mark Johnson; 12(Apr):1425−1428, 2011.
[abs][pdf]

Learning a Robust Relevance Model for Search Using Kernel Methods
Wei Wu, Jun Xu, Hang Li, Satoshi Oyama; 12(May):1429−1458, 2011.
[abs][pdf]

Computationally Efficient Convolved Multiple Output Gaussian Processes
Mauricio A. Álvarez, Neil D. Lawrence; 12(May):1459−1500, 2011.
[abs][pdf]

Learning from Partial Labels
Timothee Cour, Ben Sapp, Ben Taskar; 12(May):1501−1536, 2011.
[abs][pdf]

Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation
Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama; 12(May):1537−1586, 2011.
[abs][pdf]

Double Updating Online Learning
Peilin Zhao, Steven C.H. Hoi, Rong Jin; 12(May):1587−1615, 2011.
[abs][pdf]

Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates
Vincent Y.F. Tan, Animashree Anandkumar, Alan S. Willsky; 12(May):1617−1653, 2011.
[abs][pdf]

X-Armed Bandits
Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári; 12(May):1655−1695, 2011.
[abs][pdf]

Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets
Chiwoo Park, Jianhua Z. Huang, Yu Ding; 12(May):1697−1728, 2011.
[abs][pdf]

A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes
Stéphane Ross, Joelle Pineau, Brahim Chaib-draa, Pierre Kreitmann; 12(May):1729−1770, 2011.
[abs][pdf]

Learning Latent Tree Graphical Models
Myung Jin Choi, Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky; 12(May):1771−1812, 2011.
[abs][pdf]

Hyper-Sparse Optimal Aggregation
Stéphane Gaïffas, Guillaume Lecué; 12(Jun):1813−1833, 2011.
[abs][pdf]

A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin
Liwei Wang, Masashi Sugiyama, Zhaoxiang Jing, Cheng Yang, Zhi-Hua Zhou, Jufu Feng; 12(Jun):1835−1863, 2011.
[abs][pdf]

Stochastic Methods for l1-regularized Loss Minimization
Shai Shalev-Shwartz, Ambuj Tewari; 12(Jun):1865−1892, 2011.
[abs][pdf]

Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms
Vianney Perchet; 12(Jun):1893−1921, 2011.
[abs][pdf]

Dirichlet Process Mixtures of Generalized Linear Models
Lauren A. Hannah, David M. Blei, Warren B. Powell; 12(Jun):1923−1953, 2011.
[abs][pdf]

Kernel Regression in the Presence of Correlated Errors
Kris De Brabanter, Jos De Brabanter, Johan A.K. Suykens, Bart De Moor; 12(Jun):1955−1976, 2011.
[abs][pdf]

Generalized TD Learning
Tsuyoshi Ueno, Shin-ichi Maeda, Motoaki Kawanabe, Shin Ishii; 12(Jun):1977−2020, 2011.
[abs][pdf]

The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets
Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, Christian Buchta; 12(Jun):2021−2025, 2011.
[abs][pdf]    [code][mloss.org]

A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis
Trine Julie Abrahamsen, Lars Kai Hansen; 12(Jun):2027−2044, 2011.
[abs][pdf]

Exploiting Best-Match Equations for Efficient Reinforcement Learning
Harm van Seijen, Shimon Whiteson, Hado van Hasselt, Marco Wiering; 12(Jun):2045−2094, 2011.
[abs][pdf]

Information Rates of Nonparametric Gaussian Process Methods
Aad van der Vaart, Harry van Zanten; 12(Jun):2095−2119, 2011.
[abs][pdf]

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
John Duchi, Elad Hazan, Yoram Singer; 12(Jul):2121−2159, 2011.
[abs][pdf]

On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem
Daniil Ryabko; 12(Jul):2161−2180, 2011.
[abs][pdf]

Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood
Alexandra M. Carvalho, Teemu Roos, Arlindo L. Oliveira, Petri Myllymäki; 12(Jul):2181−2210, 2011.
[abs][pdf]

Multiple Kernel Learning Algorithms
Mehmet Gönen, Ethem Alpaydın; 12(Jul):2211−2268, 2011.
[abs][pdf]

Smoothness, Disagreement Coefficient, and the Label Complexity of Agnostic Active Learning
Liwei Wang; 12(Jul):2269−2292, 2011.
[abs][pdf]

MSVMpack: A Multi-Class Support Vector Machine Package
Fabien Lauer, Yann Guermeur; 12(Jul):2293−2296, 2011.
[abs][pdf]    [code][mloss.org]

Proximal Methods for Hierarchical Sparse Coding
Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis Bach; 12(Jul):2297−2334, 2011.
[abs][pdf]

Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models
Sharon Goldwater, Thomas L. Griffiths, Mark Johnson; 12(Jul):2335−2382, 2011.
[abs][pdf]

Waffles: A Machine Learning Toolkit
Michael Gashler; 12(Jul):2383−2387, 2011.
[abs][pdf]    [code][mloss.org]

Universality, Characteristic Kernels and RKHS Embedding of Measures
Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R.G. Lanckriet; 12(Jul):2389−2410, 2011.
[abs][pdf]

MULAN: A Java Library for Multi-Label Learning
Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek, Ioannis Vlahavas; 12(Jul):2411−2414, 2011.
[abs][pdf]    [code][mloss.org]

Union Support Recovery in Multi-task Learning
Mladen Kolar, John Lafferty, Larry Wasserman; 12(Jul):2415−2435, 2011.
[abs][pdf]

Parallel Algorithm for Learning Optimal Bayesian Network Structure
Yoshinori Tamada, Seiya Imoto, Satoru Miyano; 12(Jul):2437−2459, 2011.
[abs][pdf]

Distance Dependent Chinese Restaurant Processes
David M. Blei, Peter I. Frazier; 12(Aug):2461−2488, 2011.
[abs][pdf]

LPmade: Link Prediction Made Easy
Ryan N. Lichtenwalter, Nitesh V. Chawla; 12(Aug):2489−2492, 2011.
[abs][pdf]    [code][mloss.org]

Natural Language Processing (Almost) from Scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, Pavel Kuksa; 12(Aug):2493−2537, 2011.
[abs][pdf]

Weisfeiler-Lehman Graph Kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, Karsten M. Borgwardt; 12(Sep):2539−2561, 2011.
[abs][pdf]

Kernel Analysis of Deep Networks
Grégoire Montavon, Mikio L. Braun, Klaus-Robert Müller; 12(Sep):2563−2581, 2011.
[abs][pdf]

Theoretical Analysis of Bayesian Matrix Factorization
Shinichi Nakajima, Masashi Sugiyama; 12(Sep):2583−2648, 2011.
[abs][pdf]

Bayesian Co-Training
Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, R. Bharat Rao; 12(Sep):2649−2680, 2011.
[abs][pdf]

Convex and Network Flow Optimization for Structured Sparsity
Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis Bach; 12(Sep):2681−2720, 2011.
[abs][pdf]

Large Margin Hierarchical Classification with Mutually Exclusive Class Membership
Huixin Wang, Xiaotong Shen, Wei Pan; 12(Sep):2721−2748, 2011.
[abs][pdf]

Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes
Elias Zavitsanos, Georgios Paliouras, George A. Vouros; 12(Oct):2749−2775, 2011.
[abs][pdf]

Structured Variable Selection with Sparsity-Inducing Norms
Rodolphe Jenatton, Jean-Yves Audibert, Francis Bach; 12(Oct):2777−2824, 2011.
[abs][pdf]

Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay; 12(Oct):2825−2830, 2011.
[abs][pdf]    [code][mloss.org]

Neyman-Pearson Classification, Convexity and Stochastic Constraints
Philippe Rigollet, Xin Tong; 12(Oct):2831−2855, 2011.
[abs][pdf]

Efficient Learning with Partially Observed Attributes
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir; 12(Oct):2857−2878, 2011.
[abs][pdf]

Convergence Rates of Efficient Global Optimization Algorithms
Adam D. Bull; 12(Oct):2879−2904, 2011.
[abs][pdf]

On Equivalence Relationships Between Classification and Ranking Algorithms
Şeyda Ertekin, Cynthia Rudin; 12(Oct):2905−2929, 2011.
[abs][pdf]

Hierarchical Knowledge Gradient for Sequential Sampling
Martijn R.K. Mes, Warren B. Powell, Peter I. Frazier; 12(Oct):2931−2974, 2011.
[abs][pdf]

High-dimensional Covariance Estimation Based On Gaussian Graphical Models
Shuheng Zhou, Philipp Rütimann, Min Xu, Peter Bühlmann; 12(Oct):2975−3026, 2011.
[abs][pdf]

Robust Approximate Bilinear Programming for Value Function Approximation
Marek Petrik, Shlomo Zilberstein; 12(Oct):3027−3063, 2011.
[abs][pdf]

The Stationary Subspace Analysis Toolbox
Jan Saputra Müller, Paul von Bünau, Frank C. Meinecke, Franz J. Király, Klaus-Robert Müller; 12(Oct):3065−3069, 2011.
[abs][pdf]    [code][mloss.org]

In All Likelihood, Deep Belief Is Not Enough
Lucas Theis, Sebastian Gerwinn, Fabian Sinz, Matthias Bethge; 12(Nov):3071−3096, 2011.
[abs][pdf]

Efficient and Effective Visual Codebook Generation Using Additive Kernels
Jianxin Wu, Wei-Chian Tan, James M. Rehg; 12(Nov):3097−3118, 2011.
[abs][pdf]

Unsupervised Supervised Learning II: Margin-Based Classification Without Labels
Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon; 12(Nov):3119−3145, 2011.
[abs][pdf]

Adaptive Exact Inference in Graphical Models
Özgür Sümer, Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu; 12(Nov):3147−3186, 2011.
[abs][pdf]

Group Lasso Estimation of High-dimensional Covariance Matrices
Jérémie Bigot, Rolando J. Biscay, Jean-Michel Loubes, Lillian Muñiz-Alvarez; 12(Nov):3187−3225, 2011.
[abs][pdf]

Robust Gaussian Process Regression with a Student-t Likelihood
Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari; 12(Nov):3227−3257, 2011.
[abs][pdf]

The Sample Complexity of Dictionary Learning
Daniel Vainsencher, Shie Mannor, Alfred M. Bruckstein; 12(Nov):3259−3281, 2011.
[abs][pdf]

An Asymptotic Behaviour of the Marginal Likelihood for General Markov Models
Piotr Zwiernik; 12(Nov):3283−3310, 2011.
[abs][pdf]

Semi-Supervised Learning with Measure Propagation
Amarnag Subramanya, Jeff Bilmes; 12(Nov):3311−3370, 2011.
[abs][pdf]

Learning with Structured Sparsity
Junzhou Huang, Tong Zhang, Dimitris Metaxas; 12(Nov):3371−3412, 2011.
[abs][pdf]

A Simpler Approach to Matrix Completion
Benjamin Recht ; 12(Dec):3413−3430, 2011.
[abs][pdf]

Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms
Benoît Patra; 12(Dec):3431−3466, 2011.
[abs][pdf]




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