JMLR Volume 12

• Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation

  Yizhao Ni, Craig Saunders, Sandor Szedmak, Mahesan Niranjan (1):1−30, 2011 PDF BibTeX

• Improved Moves for Truncated Convex Models

  M. Pawan Kumar, Olga Veksler, Philip H.S. Torr (2):31−67, 2011 PDF BibTeX

• CARP: Software for Fishing Out Good Clustering Algorithms

  Volodymyr Melnykov, Ranjan Maitra (3):69−73, 2011 codePDF BibTeX

• Multitask Sparsity via Maximum Entropy Discrimination

  Tony Jebara (4):75−110, 2011 PDF BibTeX

• Bayesian Generalized Kernel Mixed Models

  Zhihua Zhang, Guang Dai, Michael I. Jordan (5):111−139, 2011 PDF BibTeX

• Training SVMs Without Offset

  Ingo Steinwart, Don Hush, Clint Scovel (6):141−202, 2011 PDF BibTeX

• Logistic Stick-Breaking Process

  Lu Ren, Lan Du, Lawrence Carin, David Dunson (7):203−239, 2011 PDF BibTeX

• Online Learning in Case of Unbounded Losses Using Follow the Perturbed Leader Algorithm

  Vladimir V. V'yugin (8):241−266, 2011 PDF BibTeX

• A Bayesian Approximation Method for Online Ranking

  Ruby C. Weng, Chih-Jen Lin (9):267−300, 2011 PDF BibTeX

• Cumulative Distribution Networks and the Derivative-sum-product Algorithm: Models and Inference for Cumulative Distribution Functions on Graphs

  Jim C. Huang, Brendan J. Frey (10):301−348, 2011 PDF BibTeX

• Models of Cooperative Teaching and Learning

  Sandra Zilles, Steffen Lange, Robert Holte, Martin Zinkevich (11):349−384, 2011 PDF BibTeX

• Operator Norm Convergence of Spectral Clustering on Level Sets

  Bruno Pelletier, Pierre Pudlo (12):385−416, 2011 PDF BibTeX

• Approximate Marginals in Latent Gaussian Models

  Botond Cseke, Tom Heskes (13):417−454, 2011 PDF BibTeX

• Posterior Sparsity in Unsupervised Dependency Parsing

  Jennifer Gillenwater, Kuzman Ganchev, João Graça, Fernando Pereira, Ben Taskar (14):455−490, 2011 PDF BibTeX

• Learning Multi-modal Similarity

  Brian McFee, Gert Lanckriet (15):491−523, 2011 PDF BibTeX

• Minimum Description Length Penalization for Group and Multi-Task Sparse Learning

  Paramveer S. Dhillon, Dean Foster, Lyle H. Ungar (16):525−564, 2011 PDF BibTeX

• Variable Sparsity Kernel Learning

  Jonathan Aflalo, Aharon Ben-Tal, Chiranjib Bhattacharyya, Jagarlapudi Saketha Nath, Sankaran Raman (17):565−592, 2011 PDF BibTeX

• Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach

  Gilles Meyer, Silvère Bonnabel, Rodolphe Sepulchre (18):593−625, 2011 PDF BibTeX

• Parameter Screening and Optimisation for ILP using Designed Experiments

  Ashwin Srinivasan, Ganesh Ramakrishnan (19):627−662, 2011 PDF BibTeX

• Efficient Structure Learning of Bayesian Networks using Constraints

  Cassio P. de Campos, Qiang Ji (20):663−689, 2011 PDF BibTeX

• Inverse Reinforcement Learning in Partially Observable Environments

  Jaedeug Choi, Kee-Eung Kim (21):691−730, 2011 PDF BibTeX

• Information, Divergence and Risk for Binary Experiments

  Mark D. Reid, Robert C. Williamson (22):731−817, 2011 PDF BibTeX

• Learning Transformation Models for Ranking and Survival Analysis

  Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel (23):819−862, 2011 PDF BibTeX

• Sparse Linear Identifiable Multivariate Modeling

  Ricardo Henao, Ole Winther (24):863−905, 2011 PDF BibTeX

• Forest Density Estimation

  Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John Lafferty, Larry Wasserman (25):907−951, 2011 PDF BibTeX

• l_p-Norm Multiple Kernel Learning

  Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Alexander Zien (26):953−997, 2011 PDF BibTeX

• Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data

  Zeeshan Syed, John Guttag (27):999−1024, 2011 PDF BibTeX

• Two Distributed-State Models For Generating High-Dimensional Time Series

  Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis (28):1025−1068, 2011 PDF BibTeX

• Differentially Private Empirical Risk Minimization

  Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate (29):1069−1109, 2011 PDF BibTeX

• Anechoic Blind Source Separation Using Wigner Marginals

  Lars Omlor, Martin A. Giese (30):1111−1148, 2011 PDF BibTeX

• Laplacian Support Vector Machines Trained in the Primal

  Stefano Melacci, Mikhail Belkin (31):1149−1184, 2011 PDF BibTeX

• The Indian Buffet Process: An Introduction and Review

  Thomas L. Griffiths, Zoubin Ghahramani (32):1185−1224, 2011 PDF BibTeX

• 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 (33):1225−1248, 2011 PDF BibTeX

• Locally Defined Principal Curves and Surfaces

  Umut Ozertem, Deniz Erdogmus (34):1249−1286, 2011 PDF BibTeX

• Better Algorithms for Benign Bandits

  Elad Hazan, Satyen Kale (35):1287−1311, 2011 PDF BibTeX

• A Family of Simple Non-Parametric Kernel Learning Algorithms

  Jinfeng Zhuang, Ivor W. Tsang, Steven C.H. Hoi (36):1313−1347, 2011 PDF BibTeX

• Faster Algorithms for Max-Product Message-Passing

  Julian J. McAuley, Tibério S. Caetano (37):1349−1388, 2011 PDF BibTeX

• Clustering Algorithms for Chains

  Antti Ukkonen (38):1389−1423, 2011 PDF BibTeX

• 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 (39):1425−1428, 2011 PDF BibTeX

• Learning a Robust Relevance Model for Search Using Kernel Methods

  Wei Wu, Jun Xu, Hang Li, Satoshi Oyama (40):1429−1458, 2011 PDF BibTeX

• Computationally Efficient Convolved Multiple Output Gaussian Processes

  Mauricio A. Álvarez, Neil D. Lawrence (41):1459−1500, 2011 PDF BibTeX

• Learning from Partial Labels

  Timothee Cour, Ben Sapp, Ben Taskar (42):1501−1536, 2011 PDF BibTeX

• Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation

  Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama (43):1537−1586, 2011 PDF BibTeX

• Double Updating Online Learning

  Peilin Zhao, Steven C.H. Hoi, Rong Jin (44):1587−1615, 2011 PDF BibTeX

• Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates

  Vincent Y.F. Tan, Animashree Anandkumar, Alan S. Willsky (45):1617−1653, 2011 PDF BibTeX

• X-Armed Bandits

  Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári (46):1655−1695, 2011 PDF BibTeX

• Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets

  Chiwoo Park, Jianhua Z. Huang, Yu Ding (47):1697−1728, 2011 PDF BibTeX

• A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes

  Stéphane Ross, Joelle Pineau, Brahim Chaib-draa, Pierre Kreitmann (48):1729−1770, 2011 PDF BibTeX

• Learning Latent Tree Graphical Models

  Myung Jin Choi, Vincent Y.F. Tan, Animashree Anandkumar, Alan S. Willsky (49):1771−1812, 2011 PDF BibTeX

• Hyper-Sparse Optimal Aggregation

  Stéphane Gaîffas, Guillaume Lecué (50):1813−1833, 2011 PDF BibTeX

• A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin

  Liwei Wang, Masashi Sugiyama, Zhaoxiang Jing, Cheng Yang, Zhi-Hua Zhou, Jufu Feng (51):1835−1863, 2011 PDF BibTeX

• Stochastic Methods for l₁-regularized Loss Minimization

  Shai Shalev-Shwartz, Ambuj Tewari (52):1865−1892, 2011 PDF BibTeX

• Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms

  Vianney Perchet (53):1893−1921, 2011 PDF BibTeX

• Dirichlet Process Mixtures of Generalized Linear Models

  Lauren A. Hannah, David M. Blei, Warren B. Powell (54):1923−1953, 2011 PDF BibTeX

• Kernel Regression in the Presence of Correlated Errors

  Kris De Brabanter, Jos De Brabanter, Johan A.K. Suykens, Bart De Moor (55):1955−1976, 2011 PDF BibTeX

• Generalized TD Learning

  Tsuyoshi Ueno, Shin-ichi Maeda, Motoaki Kawanabe, Shin Ishii (56):1977−2020, 2011 PDF BibTeX

• The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets

  Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, Christian Buchta (57):2021−2025, 2011 codePDF BibTeX

• A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis

  Trine Julie Abrahamsen, Lars Kai Hansen (58):2027−2044, 2011 PDF BibTeX

• Exploiting Best-Match Equations for Efficient Reinforcement Learning

  Harm van Seijen, Shimon Whiteson, Hado van Hasselt, Marco Wiering (59):2045−2094, 2011 PDF BibTeX

• Information Rates of Nonparametric Gaussian Process Methods

  Aad van der Vaart, Harry van Zanten (60):2095−2119, 2011 PDF BibTeX

• Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

  John Duchi, Elad Hazan, Yoram Singer (61):2121−2159, 2011 PDF BibTeX

• On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem

  Daniil Ryabko (62):2161−2180, 2011 PDF BibTeX

• Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood

  Alexandra M. Carvalho, Teemu Roos, Arlindo L. Oliveira, Petri Myllymäki (63):2181−2210, 2011 PDF BibTeX

• Multiple Kernel Learning Algorithms

  Mehmet Gönen, Ethem Alpaydin (64):2211−2268, 2011 PDF BibTeX

• Smoothness, Disagreement Coefficient, and the Label Complexity of Agnostic Active Learning

  Liwei Wang (65):2269−2292, 2011 PDF BibTeX

• MSVMpack: A Multi-Class Support Vector Machine Package

  Fabien Lauer, Yann Guermeur (66):2293−2296, 2011 codePDF BibTeX

• Proximal Methods for Hierarchical Sparse Coding

  Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis Bach (67):2297−2334, 2011 PDF BibTeX

• Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models

  Sharon Goldwater, Thomas L. Griffiths, Mark Johnson (68):2335−2382, 2011 PDF BibTeX

• Waffles: A Machine Learning Toolkit

  Michael Gashler (69):2383−2387, 2011 codePDF BibTeX

• Universality, Characteristic Kernels and RKHS Embedding of Measures

  Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R.G. Lanckriet (70):2389−2410, 2011 PDF BibTeX

• MULAN: A Java Library for Multi-Label Learning

  Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek, Ioannis Vlahavas (71):2411−2414, 2011 codePDF BibTeX

• Union Support Recovery in Multi-task Learning

  Mladen Kolar, John Lafferty, Larry Wasserman (72):2415−2435, 2011 PDF BibTeX

• Parallel Algorithm for Learning Optimal Bayesian Network Structure

  Yoshinori Tamada, Seiya Imoto, Satoru Miyano (73):2437−2459, 2011 PDF BibTeX

• Distance Dependent Chinese Restaurant Processes

  David M. Blei, Peter I. Frazier (74):2461−2488, 2011 PDF BibTeX

• LPmade: Link Prediction Made Easy

  Ryan N. Lichtenwalter, Nitesh V. Chawla (75):2489−2492, 2011 codePDF BibTeX

• Natural Language Processing (Almost) from Scratch

  Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, Pavel Kuksa (76):2493−2537, 2011 PDF BibTeX

• Weisfeiler-Lehman Graph Kernels

  Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, Karsten M. Borgwardt (77):2539−2561, 2011 PDF BibTeX

• Kernel Analysis of Deep Networks

  Grégoire Montavon, Mikio L. Braun, Klaus-Robert Müller (78):2563−2581, 2011 PDF BibTeX

• Theoretical Analysis of Bayesian Matrix Factorization

  Shinichi Nakajima, Masashi Sugiyama (79):2583−2648, 2011 PDF BibTeX

• Bayesian Co-Training

  Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, R. Bharat Rao (80):2649−2680, 2011 PDF BibTeX

• Convex and Network Flow Optimization for Structured Sparsity

  Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis Bach (81):2681−2720, 2011 PDF BibTeX

• Large Margin Hierarchical Classification with Mutually Exclusive Class Membership

  Huixin Wang, Xiaotong Shen, Wei Pan (82):2721−2748, 2011 PDF BibTeX

• Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes

  Elias Zavitsanos, Georgios Paliouras, George A. Vouros (83):2749−2775, 2011 PDF BibTeX

• Structured Variable Selection with Sparsity-Inducing Norms

  Rodolphe Jenatton, Jean-Yves Audibert, Francis Bach (84):2777−2824, 2011 PDF BibTeX

• 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 (85):2825−2830, 2011 codePDF BibTeX

• Neyman-Pearson Classification, Convexity and Stochastic Constraints

  Philippe Rigollet, Xin Tong (86):2831−2855, 2011 PDF BibTeX

• Efficient Learning with Partially Observed Attributes

  Nicoló Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir (87):2857−2878, 2011 PDF BibTeX

• Convergence Rates of Efficient Global Optimization Algorithms

  Adam D. Bull (88):2879−2904, 2011 PDF BibTeX

• On Equivalence Relationships Between Classification and Ranking Algorithms

  Şeyda Ertekin, Cynthia Rudin (89):2905−2929, 2011 PDF BibTeX

• Hierarchical Knowledge Gradient for Sequential Sampling

  Martijn R.K. Mes, Warren B. Powell, Peter I. Frazier (90):2931−2974, 2011 PDF BibTeX

• High-dimensional Covariance Estimation Based On Gaussian Graphical Models

  Shuheng Zhou, Philipp Rütimann, Min Xu, Peter Bühlmann (91):2975−3026, 2011 PDF BibTeX

• Robust Approximate Bilinear Programming for Value Function Approximation

  Marek Petrik, Shlomo Zilberstein (92):3027−3063, 2011 PDF BibTeX

• The Stationary Subspace Analysis Toolbox

  Jan Saputra Müller, Paul von Bünau, Frank C. Meinecke, Franz J. Király, Klaus-Robert Müller (93):3065−3069, 2011 codePDF BibTeX

• In All Likelihood, Deep Belief Is Not Enough

  Lucas Theis, Sebastian Gerwinn, Fabian Sinz, Matthias Bethge (94):3071−3096, 2011 PDF BibTeX

• Efficient and Effective Visual Codebook Generation Using Additive Kernels

  Jianxin Wu, Wei-Chian Tan, James M. Rehg (95):3097−3118, 2011 PDF BibTeX

• Unsupervised Supervised Learning II: Margin-Based Classification Without Labels

  Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon (96):3119−3145, 2011 PDF BibTeX

• Adaptive Exact Inference in Graphical Models

  Özgür Sümer, Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu (97):3147−3186, 2011 PDF BibTeX

• Group Lasso Estimation of High-dimensional Covariance Matrices

  Jérémie Bigot, Rolando J. Biscay, Jean-Michel Loubes, Lillian Muñiz-Alvarez (98):3187−3225, 2011 PDF BibTeX

• Robust Gaussian Process Regression with a Student-t Likelihood

  Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari (99):3227−3257, 2011 PDF BibTeX

• The Sample Complexity of Dictionary Learning

  Daniel Vainsencher, Shie Mannor, Alfred M. Bruckstein (100):3259−3281, 2011 PDF BibTeX

• An Asymptotic Behaviour of the Marginal Likelihood for General Markov Models

  Piotr Zwiernik (101):3283−3310, 2011 PDF BibTeX

• Semi-Supervised Learning with Measure Propagation

  Amarnag Subramanya, Jeff Bilmes (102):3311−3370, 2011 PDF BibTeX

• Learning with Structured Sparsity

  Junzhou Huang, Tong Zhang, Dimitris Metaxas (103):3371−3412, 2011 PDF BibTeX

• A Simpler Approach to Matrix Completion

  Benjamin Recht (104):3413−3430, 2011 PDF BibTeX

• Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms

  Benoît Patra (105):3431−3466, 2011 PDF BibTeX