Journal of Machine Learning Research, Volume 12

Volume 12, 2011

• Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan:
  Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation. 1-30
  
• M. Pawan Kumar, Olga Veksler, Philip H. S. Torr:
  Improved Moves for Truncated Convex Models. 31-67
  
• Volodymyr Melnykov, Ranjan Maitra:
  CARP: Software for Fishing Out Good Clustering Algorithms. 69-73
  
• Tony Jebara:
  Multitask Sparsity via Maximum Entropy Discrimination. 75-110
  
• Zhihua Zhang, Guang Dai, Michael I. Jordan:
  Bayesian Generalized Kernel Mixed Models. 111-139
  
• Ingo Steinwart, Don R. Hush, Clint Scovel:
  Training SVMs Without Offset. 141-202
  
• Lu Ren, Lan Du, Lawrence Carin, David B. Dunson:
  Logistic Stick-Breaking Process. 203-239
  
• Vladimir V. V'yugin:
  Online Learning in Case of Unbounded Losses Using Follow the Perturbed Leader Algorithm. 241-266
  
• Ruby C. Weng, Chih-Jen Lin:
  A Bayesian Approximation Method for Online Ranking. 267-300
  
• Jim C. Huang, Brendan J. Frey:
  Cumulative Distribution Networks and the Derivative-sum-product Algorithm: Models and Inference for Cumulative Distribution Functions on Graphs. 301-348
  
• Sandra Zilles, Steffen Lange, Robert Holte, Martin Zinkevich:
  Models of Cooperative Teaching and Learning. 349-384
  
• Bruno Pelletier, Pierre Pudlo:
  Operator Norm Convergence of Spectral Clustering on Level Sets. 385-416
  
• Botond Cseke, Tom Heskes:
  Approximate Marginals in Latent Gaussian Models. 417-454
  
• Jennifer Gillenwater, Kuzman Ganchev, João Graça, Fernando Pereira, Ben Taskar:
  Posterior Sparsity in Unsupervised Dependency Parsing. 455-490
  
• Brian McFee, Gert R. G. Lanckriet:
  Learning Multi-modal Similarity. 491-523
  
• Paramveer S. Dhillon, Dean P. Foster, Lyle H. Ungar:
  Minimum Description Length Penalization for Group and Multi-Task Sparse Learning. 525-564
  
• Jonathan Aflalo, Aharon Ben-Tal, Chiranjib Bhattacharyya, Jagarlapudi Saketha Nath, Sankaran Raman:
  Variable Sparsity Kernel Learning. 565-592
  
• Gilles Meyer, Silvere Bonnabel, Rodolphe Sepulchre:
  Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach. 593-625
  
• Ashwin Srinivasan, Ganesh Ramakrishnan:
  Parameter Screening and Optimisation for ILP using Designed Experiments. 627-662
  
• Cassio Polpo de Campos, Qiang Ji:
  Efficient Structure Learning of Bayesian Networks using Constraints. 663-689
  
• Jaedeug Choi, Kee-Eung Kim:
  Inverse Reinforcement Learning in Partially Observable Environments. 691-730
  
• Mark D. Reid, Robert C. Williamson:
  Information, Divergence and Risk for Binary Experiments. 731-817
  
• Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel:
  Learning Transformation Models for Ranking and Survival Analysis. 819-862
  
• Ricardo Henao, Ole Winther:
  Sparse Linear Identifiable Multivariate Modeling. 863-905
  
• Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John D. Lafferty, Larry A. Wasserman:
  Forest Density Estimation. 907-951
  
• Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Alexander Zien:
  l_p-Norm Multiple Kernel Learning. 953-997
  
• Zeeshan Syed, John V. Guttag:
  Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data. 999-1024
  
• Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis:
  Two Distributed-State Models For Generating High-Dimensional Time Series. 1025-1068
  
• Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate:
  Differentially Private Empirical Risk Minimization. 1069-1109
  
• Lars Omlor, Martin A. Giese:
  Anechoic Blind Source Separation Using Wigner Marginals. 1111-1148
  
• Stefano Melacci, Mikhail Belkin:
  Laplacian Support Vector Machines Trained in the Primal. 1149-1184
  
• Thomas L. Griffiths, Zoubin Ghahramani:
  The Indian Buffet Process: An Introduction and Review. 1185-1224
  
• Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen:
  DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. 1225-1248
  
• Umut Ozertem, Deniz Erdogmus:
  Locally Defined Principal Curves and Surfaces. 1249-1286
  
• Elad Hazan, Satyen Kale:
  Better Algorithms for Benign Bandits. 1287-1311
  
• Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi:
  A Family of Simple Non-Parametric Kernel Learning Algorithms. 1313-1347
  
• Julian John McAuley, Tibério S. Caetano:
  Faster Algorithms for Max-Product Message-Passing. 1349-1388
  
• Antti Ukkonen:
  Clustering Algorithms for Chains. 1389-1423
  
• Dorota Glowacka, John Shawe-Taylor, Alex Clark, Colin de la Higuera, Mark Johnson:
  Introduction to the Special Topic on Grammar Induction, Representation of Language and Language Learning. 1425-1428
  
• Wei Wu, Jun Xu, Hang Li, Satoshi Oyama:
  Learning a Robust Relevance Model for Search Using Kernel Methods. 1429-1458
  
• Mauricio A. Álvarez, Neil D. Lawrence:
  Computationally Efficient Convolved Multiple Output Gaussian Processes. 1459-1500
  
• Timothée Cour, Benjamin Sapp, Ben Taskar:
  Learning from Partial Labels. 1501-1536
  
• Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama:
  Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation. 1537-1586
  
• Peilin Zhao, Steven C. H. Hoi, Rong Jin:
  Double Updating Online Learning. 1587-1615
  
• Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
  Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates. 1617-1653
  
• Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári:
  X-Armed Bandits. 1655-1695
  
• Chiwoo Park, Jianhua Z. Huang, Yu Ding:
  Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets. 1697-1728
  
• Stéphane Ross, Joelle Pineau, Brahim Chaib-draa, Pierre Kreitmann:
  A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes. 1729-1770
  
• Myung Jin Choi, Vincent Y. F. Tan, Animashree Anandkumar, Alan S. Willsky:
  Learning Latent Tree Graphical Models. 1771-1812
  
• Stéphane Gaïffas, Guillaume Lecué:
  Hyper-Sparse Optimal Aggregation. 1813-1833
  
• Liwei Wang, Masashi Sugiyama, Zhaoxiang Jing, Cheng Yang, Zhi-Hua Zhou, Jufu Feng:
  A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin. 1835-1863
  
• Shai Shalev-Shwartz, Ambuj Tewari:
  Stochastic Methods for l₁-regularized Loss Minimization. 1865-1892
  
• Vianney Perchet:
  Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms. 1893-1921
  
• Lauren Hannah, David M. Blei, Warren B. Powell:
  Dirichlet Process Mixtures of Generalized Linear Models. 1923-1953
  
• Kris De Brabanter, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor:
  Kernel Regression in the Presence of Correlated Errors. 1955-1976
  
• Tsuyoshi Ueno, Shin-ichi Maeda, Motoaki Kawanabe, Shin Ishii:
  Generalized TD Learning. 1977-2020
  
• Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, Christian Buchta:
  The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets. 2021-2025
  
• Trine Julie Abrahamsen, Lars Kai Hansen:
  A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis. 2027-2044
  
• Harm van Seijen, Shimon Whiteson, Hado van Hasselt, Marco Wiering:
  Exploiting Best-Match Equations for Efficient Reinforcement Learning. 2045-2094
  
• Aad van der Vaart, Harry van Zanten:
  Information Rates of Nonparametric Gaussian Process Methods. 2095-2119
  
• John C. Duchi, Elad Hazan, Yoram Singer:
  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. 2121-2159
  
• Daniil Ryabko:
  On the Relation between Realizable and Nonrealizable Cases of the Sequence Prediction Problem. 2161-2180
  
• Alexandra M. Carvalho, Teemu Roos, Arlindo L. Oliveira, Petri Myllymäki:
  Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood. 2181-2210
  
• Mehmet Gönen, Ethem Alpaydin:
  Multiple Kernel Learning Algorithms. 2211-2268
  
• Liwei Wang:
  Smoothness, Disagreement Coefficient, and the Label Complexity of Agnostic Active Learning. 2269-2292
  
• Fabien Lauer, Yann Guermeur:
  MSVMpack: A Multi-Class Support Vector Machine Package. 2293-2296
  
• Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis Bach:
  Proximal Methods for Hierarchical Sparse Coding. 2297-2334
  
• Sharon Goldwater, Thomas L. Griffiths, Mark Johnson:
  Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models. 2335-2382
  
• Michael Gashler:
  Waffles: A Machine Learning Toolkit. 2383-2387
  
• Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R. G. Lanckriet:
  Universality, Characteristic Kernels and RKHS Embedding of Measures. 2389-2410
  
• Grigorios Tsoumakas, Eleftherios Spyromitros Xioufis, Jozef Vilcek, Ioannis P. Vlahavas:
  MULAN: A Java Library for Multi-Label Learning. 2411-2414
  
• Mladen Kolar, John D. Lafferty, Larry A. Wasserman:
  Union Support Recovery in Multi-task Learning. 2415-2435
  
• Yoshinori Tamada, Seiya Imoto, Satoru Miyano:
  Parallel Algorithm for Learning Optimal Bayesian Network Structure. 2437-2459
  
• David M. Blei, Peter Frazier:
  Distance Dependent Chinese Restaurant Processes. 2461-2488
  
• Ryan Lichtenwalter, Nitesh V. Chawla:
  LPmade: Link Prediction Made Easy. 2489-2492
  
• Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, Pavel P. Kuksa:
  Natural Language Processing (Almost) from Scratch. 2493-2537
  
• Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, Karsten M. Borgwardt:
  Weisfeiler-Lehman Graph Kernels. 2539-2561
  
• Grégoire Montavon, Mikio L. Braun, Klaus-Robert Müller:
  Kernel Analysis of Deep Networks. 2563-2581
  
• Shinichi Nakajima, Masashi Sugiyama:
  Theoretical Analysis of Bayesian Matrix Factorization. 2583-2648
  
• Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, R. Bharat Rao:
  Bayesian Co-Training. 2649-2680
  
• Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis Bach:
  Convex and Network Flow Optimization for Structured Sparsity. 2681-2720
  
• Huixin Wang, Xiaotong Shen, Wei Pan:
  Large Margin Hierarchical Classification with Mutually Exclusive Class Membership. 2721-2748
  
• Elias Zavitsanos, Georgios Paliouras, George A. Vouros:
  Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes. 2749-2775
  
• Rodolphe Jenatton, Jean-Yves Audibert, Francis Bach:
  Structured Variable Selection with Sparsity-Inducing Norms. 2777-2824
  
• 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, Edouard Duchesnay:
  Scikit-learn: Machine Learning in Python. 2825-2830
  
• Philippe Rigollet, Xin Tong:
  Neyman-Pearson Classification, Convexity and Stochastic Constraints. 2831-2855
  
• Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir:
  Efficient Learning with Partially Observed Attributes. 2857-2878
  
• Adam D. Bull:
  Convergence Rates of Efficient Global Optimization Algorithms. 2879-2904
  
• Seyda Ertekin, Cynthia Rudin:
  On Equivalence Relationships Between Classification and Ranking Algorithms. 2905-2929
  
• Martijn R. K. Mes, Warren B. Powell, Peter Frazier:
  Hierarchical Knowledge Gradient for Sequential Sampling. 2931-2974
  
• Shuheng Zhou, Philipp Rütimann, Min Xu, Peter Bühlmann:
  High-dimensional Covariance Estimation Based On Gaussian Graphical Models. 2975-3026
  
• Marek Petrik, Shlomo Zilberstein:
  Robust Approximate Bilinear Programming for Value Function Approximation. 3027-3063
  
• Jan Saputra Müller, Paul von Bünau, Frank C. Meinecke, Franz J. Király, Klaus-Robert Müller:
  The Stationary Subspace Analysis Toolbox. 3065-3069
  
• Lucas Theis, Sebastian Gerwinn, Fabian H. Sinz, Matthias Bethge:
  In All Likelihood, Deep Belief Is Not Enough. 3071-3096
  
• Jianxin Wu, Wei-Chian Tan, James M. Rehg:
  Efficient and Effective Visual Codebook Generation Using Additive Kernels. 3097-3118
  
• Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon:
  Unsupervised Supervised Learning II: Margin-Based Classification Without Labels. 3119-3145
  
• Özgür Sümer, Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu:
  Adaptive Exact Inference in Graphical Models. 3147-3186
  
• Jérémie Bigot, Rolando J. Biscay, Jean-Michel Loubes, Lillian Muñiz-Alvarez:
  Group Lasso Estimation of High-dimensional Covariance Matrices. 3187-3225
  
• Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari:
  Robust Gaussian Process Regression with a Student-t Likelihood. 3227-3257
  
• Daniel Vainsencher, Shie Mannor, Alfred M. Bruckstein:
  The Sample Complexity of Dictionary Learning. 3259-3281
  
• Piotr Zwiernik:
  An Asymptotic Behaviour of the Marginal Likelihood for General Markov Models. 3283-3310
  
• Amarnag Subramanya, Jeff Bilmes:
  Semi-Supervised Learning with Measure Propagation. 3311-3370
  
• Junzhou Huang, Tong Zhang, Dimitris N. Metaxas:
  Learning with Structured Sparsity. 3371-3412
  
• Benjamin Recht:
  A Simpler Approach to Matrix Completion. 3413-3430
  
• Benoît Patra:
  Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms. 3431-3466
  

Last update Wed May 22 17:59:39 2013 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page