ML Anthology

JMLR 2011

97 papers

A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes Stéphane Ross, Joelle Pineau, Brahim Chaib-draa, Pierre Kreitmann
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A Bayesian Approximation Method for Online Ranking Ruby C. Weng, Chih-Jen Lin
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A Cure for Variance Inflation in High Dimensional Kernel Principal Component Analysis Trine Julie Abrahamsen, Lars Kai Hansen
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A Family of Simple Non-Parametric Kernel Learning Algorithms Jinfeng Zhuang, Ivor W. Tsang, Steven C.H. Hoi
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A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin Liwei Wang, Masashi Sugiyama, Zhaoxiang Jing, Cheng Yang, Zhi-Hua Zhou, Jufu Feng
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A Simpler Approach to Matrix Completion Benjamin Recht
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Adaptive Exact Inference in Graphical Models Özgür Sümer, Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu
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Adaptive Subgradient Methods for Online Learning and Stochastic Optimization John Duchi, Elad Hazan, Yoram Singer
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An Asymptotic Behaviour of the Marginal Likelihood for General Markov Models Piotr Zwiernik
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Anechoic Blind Source Separation Using Wigner Marginals Lars Omlor, Martin A. Giese
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Approximate Marginals in Latent Gaussian Models Botond Cseke, Tom Heskes
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Bayesian Co-Training Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, R. Bharat Rao
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Bayesian Generalized Kernel Mixed Models Zhihua Zhang, Guang Dai, Michael I. Jordan
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Better Algorithms for Benign Bandits Elad Hazan, Satyen Kale
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Clustering Algorithms for Chains Antti Ukkonen
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Computationally Efficient Convolved Multiple Output Gaussian Processes Mauricio A. Álvarez, Neil D. Lawrence
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Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms Benoît Patra
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Convergence Rates of Efficient Global Optimization Algorithms Adam D. Bull
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Convex and Network Flow Optimization for Structured Sparsity Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis Bach
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Cumulative Distribution Networks and the Derivative-Sum-Product Algorithm: Models and Inference for Cumulative Distribution Functions on Graphs Jim C. Huang, Brendan J. Frey
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Differentially Private Empirical Risk Minimization Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate
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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
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Dirichlet Process Mixtures of Generalized Linear Models Lauren A. Hannah, David M. Blei, Warren B. Powell
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Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood Alexandra M. Carvalho, Teemu Roos, Arlindo L. Oliveira, Petri Myllymäki
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Distance Dependent Chinese Restaurant Processes David M. Blei, Peter I. Frazier
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Domain Decomposition Approach for Fast Gaussian Process Regression of Large Spatial Data Sets Chiwoo Park, Jianhua Z. Huang, Yu Ding
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Double Updating Online Learning Peilin Zhao, Steven C.H. Hoi, Rong Jin
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Efficient and Effective Visual Codebook Generation Using Additive Kernels Jianxin Wu, Wei-Chian Tan, James M. Rehg
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Efficient Learning with Partially Observed Attributes Nicoló Cesa-Bianchi, Shai Shalev-Shwartz, Ohad Shamir
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Efficient Structure Learning of Bayesian Networks Using Constraints Cassio P. de Campos, Qiang Ji
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Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation Yizhao Ni, Craig Saunders, Sandor Szedmak, Mahesan Niranjan
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Exploiting Best-Match Equations for Efficient Reinforcement Learning Harm van Seijen, Shimon Whiteson, Hado van Hasselt, Marco Wiering
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Faster Algorithms for Max-Product Message-Passing Julian J. McAuley, Tibério S. Caetano
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Forest Density Estimation Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John Lafferty, Larry Wasserman
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Generalized TD Learning Tsuyoshi Ueno, Shin-ichi Maeda, Motoaki Kawanabe, Shin Ishii
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Group Lasso Estimation of High-Dimensional Covariance Matrices Jérémie Bigot, Rolando J. Biscay, Jean-Michel Loubes, Lillian Muñiz-Alvarez
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Hierarchical Knowledge Gradient for Sequential Sampling Martijn R.K. Mes, Warren B. Powell, Peter I. Frazier
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High-Dimensional Covariance Estimation Based on Gaussian Graphical Models Shuheng Zhou, Philipp Rütimann, Min Xu, Peter Bühlmann
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Hyper-Sparse Optimal Aggregation Stéphane Gaîffas, Guillaume Lecué
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Improved Moves for Truncated Convex Models M. Pawan Kumar, Olga Veksler, Philip H.S. Torr
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In All Likelihood, Deep Belief Is Not Enough Lucas Theis, Sebastian Gerwinn, Fabian Sinz, Matthias Bethge
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Information Rates of Nonparametric Gaussian Process Methods Aad van der Vaart, Harry van Zanten
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Information, Divergence and Risk for Binary Experiments Mark D. Reid, Robert C. Williamson
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Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms Vianney Perchet
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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
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Inverse Reinforcement Learning in Partially Observable Environments Jaedeug Choi, Kee-Eung Kim
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Kernel Analysis of Deep Networks Grégoire Montavon, Mikio L. Braun, Klaus-Robert Müller
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Kernel Regression in the Presence of Correlated Errors Kris De Brabanter, Jos De Brabanter, Johan A.K. Suykens, Bart De Moor
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Laplacian Support Vector Machines Trained in the Primal Stefano Melacci, Mikhail Belkin
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Large Margin Hierarchical Classification with Mutually Exclusive Class Membership Huixin Wang, Xiaotong Shen, Wei Pan
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Learning a Robust Relevance Model for Search Using Kernel Methods Wei Wu, Jun Xu, Hang Li, Satoshi Oyama
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Learning from Partial Labels Timothee Cour, Ben Sapp, Ben Taskar
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Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates Vincent Y.F. Tan, Animashree Anandkumar, Alan S. Willsky
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Learning Latent Tree Graphical Models Myung Jin Choi, Vincent Y.F. Tan, Animashree Anandkumar, Alan S. Willsky
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Learning Multi-Modal Similarity Brian McFee, Gert Lanckriet
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Learning Transformation Models for Ranking and Survival Analysis Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel
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Learning with Structured Sparsity Junzhou Huang, Tong Zhang, Dimitris Metaxas
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Locally Defined Principal Curves and Surfaces Umut Ozertem, Deniz Erdogmus
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Logistic Stick-Breaking Process Lu Ren, Lan Du, Lawrence Carin, David Dunson
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Lp-Norm Multiple Kernel Learning Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Alexander Zien
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Minimum Description Length Penalization for Group and Multi-Task Sparse Learning Paramveer S. Dhillon, Dean Foster, Lyle H. Ungar
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Models of Cooperative Teaching and Learning Sandra Zilles, Steffen Lange, Robert Holte, Martin Zinkevich
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Multiple Kernel Learning Algorithms Mehmet Gönen, Ethem Alpaydin
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Multitask Sparsity via Maximum Entropy Discrimination Tony Jebara
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Natural Language Processing (Almost) from Scratch Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, Pavel Kuksa
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Neyman-Pearson Classification, Convexity and Stochastic Constraints Philippe Rigollet, Xin Tong
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Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes Elias Zavitsanos, Georgios Paliouras, George A. Vouros
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On Equivalence Relationships Between Classification and Ranking Algorithms Şeyda Ertekin, Cynthia Rudin
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On the Relation Between Realizable and Nonrealizable Cases of the Sequence Prediction Problem Daniil Ryabko
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Online Learning in Case of Unbounded Losses Using Follow the Perturbed Leader Algorithm Vladimir V. V'yugin
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Operator Norm Convergence of Spectral Clustering on Level Sets Bruno Pelletier, Pierre Pudlo
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Parallel Algorithm for Learning Optimal Bayesian Network Structure Yoshinori Tamada, Seiya Imoto, Satoru Miyano
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Parameter Screening and Optimisation for ILP Using Designed Experiments Ashwin Srinivasan, Ganesh Ramakrishnan
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Posterior Sparsity in Unsupervised Dependency Parsing Jennifer Gillenwater, Kuzman Ganchev, João Graça, Fernando Pereira, Ben Taskar
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Producing Power-Law Distributions and Damping Word Frequencies with Two-Stage Language Models Sharon Goldwater, Thomas L. Griffiths, Mark Johnson
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Proximal Methods for Hierarchical Sparse Coding Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis Bach
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Regression on Fixed-Rank Positive Semidefinite Matrices: A Riemannian Approach Gilles Meyer, Silvère Bonnabel, Rodolphe Sepulchre
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Robust Approximate Bilinear Programming for Value Function Approximation Marek Petrik, Shlomo Zilberstein
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Robust Gaussian Process Regression with a Student-T Likelihood Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari
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Semi-Supervised Learning with Measure Propagation Amarnag Subramanya, Jeff Bilmes
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Smoothness, Disagreement Coefficient, and the Label Complexity of Agnostic Active Learning Liwei Wang
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Sparse Linear Identifiable Multivariate Modeling Ricardo Henao, Ole Winther
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Stochastic Methods for L1-Regularized Loss Minimization Shai Shalev-Shwartz, Ambuj Tewari
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Structured Variable Selection with Sparsity-Inducing Norms Rodolphe Jenatton, Jean-Yves Audibert, Francis Bach
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Super-Linear Convergence of Dual Augmented Lagrangian Algorithm for Sparsity Regularized Estimation Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama
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The Indian Buffet Process: An Introduction and Review Thomas L. Griffiths, Zoubin Ghahramani
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The Sample Complexity of Dictionary Learning Daniel Vainsencher, Shie Mannor, Alfred M. Bruckstein
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Theoretical Analysis of Bayesian Matrix Factorization Shinichi Nakajima, Masashi Sugiyama
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Training SVMs Without Offset Ingo Steinwart, Don Hush, Clint Scovel
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Two Distributed-State Models for Generating High-Dimensional Time Series Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis
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Union Support Recovery in Multi-Task Learning Mladen Kolar, John Lafferty, Larry Wasserman
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Universality, Characteristic Kernels and RKHS Embedding of Measures Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R.G. Lanckriet
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Unsupervised Similarity-Based Risk Stratification for Cardiovascular Events Using Long-Term Time-Series Data Zeeshan Syed, John Guttag
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Unsupervised Supervised Learning II: Margin-Based Classification Without Labels Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon
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Variable Sparsity Kernel Learning Jonathan Aflalo, Aharon Ben-Tal, Chiranjib Bhattacharyya, Jagarlapudi Saketha Nath, Sankaran Raman
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Weisfeiler-Lehman Graph Kernels Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, Karsten M. Borgwardt
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X-Armed Bandits Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári
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