JMLR 2011

105 papers

Convergence of Distributed Asynchronous Learning Vector Quantization Algorithms

Benoît Patra

A Simpler Approach to Matrix Completion

Benjamin Recht

Learning with Structured Sparsity

Junzhou Huang, Tong Zhang, Dimitris Metaxas

Semi-Supervised Learning with Measure Propagation

Amarnag Subramanya, Jeff Bilmes

An Asymptotic Behaviour of the Marginal Likelihood for General Markov Models

Piotr Zwiernik

The Sample Complexity of Dictionary Learning

Daniel Vainsencher, Shie Mannor, Alfred M. Bruckstein

Robust Gaussian Process Regression with a Student- Likelihood

Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari

Group Lasso Estimation of High-dimensional Covariance Matrices

Jérémie Bigot, Rolando J. Biscay, Jean-Michel Loubes, Lillian Muñiz-Alvarez

Adaptive Exact Inference in Graphical Models

Ozgur Sumer, Umut A. Acar, Alexander T. Ihler, Ramgopal R. Mettu

Unsupervised Supervised Learning II: Margin-Based Classification Without Labels

Krishnakumar Balasubramanian, Pinar Donmez, Guy Lebanon

Efficient and Effective Visual Codebook Generation Using Additive Kernels

Jianxin Wu, Wei-Chian Tan, James M. Rehg

In All Likelihood, Deep Belief Is Not Enough

Lucas Theis, Sebastian Gerwinn, Fabian Sinz, Matthias Bethge

The Stationary Subspace Analysis Toolbox

Jan Saputra Müller, Paul von Bünau, Frank C. Meinecke, Franz J. Király, Klaus-Robert Müller

Robust Approximate Bilinear Programming for Value Function Approximation

Marek Petrik, Shlomo Zilberstein

High-dimensional Covariance Estimation Based On Gaussian Graphical Models

Shuheng Zhou, Philipp Rütimann, Min Xu, Peter Bühlmann

Hierarchical Knowledge Gradient for Sequential Sampling

Martijn R.K. Mes, Warren B. Powell, Peter I. Frazier

On Equivalence Relationships Between Classification and Ranking Algorithms

Seyda Ertekin, Cynthia Rudin

Convergence Rates of Efficient Global Optimization Algorithms

Adam D. Bull

Efficient Learning with Partially Observed Attributes

Nicolò Cesa-bianchi, Shai Shalev-shwartz, Ohad Shamir

Neyman-Pearson Classification, Convexity and Stochastic Constraints

Philippe Rigollet, Xin Tong

Scikit-learn: Machine Learning in Python

Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion

Structured Variable Selection with Sparsity-Inducing Norms

Rodolphe Jenatton, Jean-yves Audibert, Francis Bach

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

Elias Zavitsanos, Georgios Paliouras, George A. Vouros

Large Margin Hierarchical Classification with Mutually Exclusive Class Membership

Huixin Wang, Xiaotong Shen, Wei Pan

Convex and Network Flow Optimization for Structured Sparsity

Julien Mairal, Rodolphe Jenatton, Guillaume Obozinski, Francis Bach

Bayesian Co-Training

Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, R. Bharat Rao

Theoretical Analysis of Bayesian Matrix Factorization

Shinichi Nakajima, Masashi Sugiyama

Kernel Analysis of Deep Networks

Grégoire Montavon, Mikio L. Braun, Klaus-Robert Müller

Weisfeiler-Lehman Graph Kernels

Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, Karsten M. Borgwardt

Natural Language Processing (Almost) from Scratch

Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu

LPmade: Link Prediction Made Easy

Ryan N. Lichtenwalter, Nitesh V. Chawla

Distance Dependent Chinese Restaurant Processes

David M. Blei, Peter I. Frazier

Parallel Algorithm for Learning Optimal Bayesian Network Structure

Yoshinori Tamada, Seiya Imoto, Satoru Miyano

Union Support Recovery in Multi-task Learning

Mladen Kolar, John Lafferty, Larry Wasserman

MULAN: A Java Library for Multi-Label Learning

Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek, Ioannis Vlahavas

Universality, Characteristic Kernels and RKHS Embedding of Measures

Bharath K. Sriperumbudur, Kenji Fukumizu, Gert R.G. Lanckriet

Waffles: A Machine Learning Toolkit

Michael Gashler

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

Sharon Goldwater, Thomas L. Griffiths, Mark Johnson

Proximal Methods for Hierarchical Sparse Coding

Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis Bach

MSVMpack: A Multi-Class Support Vector Machine Package

Fabien Lauer, Yann Guermeur

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

Liwei Wang

Multiple Kernel Learning Algorithms

Mehmet Gönen, Ethem Alpaydin

Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood

Alexandra M. Carvalho, Teemu Roos, Arlindo L. Oliveira, Petri Myllym&#228

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

Daniil Ryabko

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization

John Duchi, Elad Hazan, Yoram Singer

Information Rates of Nonparametric Gaussian Process Methods

Aad van der Vaart, Harry van Zanten

Exploiting Best-Match Equations for Efficient Reinforcement Learning

Harm Van Seijen, Shimon Whiteson, Hado van Hasselt, Marco Wiering

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

Trine Julie Abrahamsen, Lars Kai Hansen

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

Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, Christian Buchta

Generalized TD Learning

Tsuyoshi Ueno, Shin-ichi Maeda, Motoaki Kawanabe, Shin Ishii

Kernel Regression in the Presence of Correlated Errors

Kris De Brabanter, Jos De Brabanter, Johan A.K. Suykens, Bart De Moor

Dirichlet Process Mixtures of Generalized Linear Models

Lauren A. Hannah, David M. Blei, Warren B. Powell

Internal Regret with Partial Monitoring: Calibration-Based Optimal Algorithms

Vianney Perchet

Stochastic Methods for -regularized Loss Minimization

Shai Shalev-shwartz, Ambuj Tewari

A Refined Margin Analysis for Boosting Algorithms via Equilibrium Margin

Liwei Wang, Masashi Sugiyama, Zhaoxiang Jing, Cheng Yang, Zhi-Hua Zhou

Hyper-Sparse Optimal Aggregation

Stephane Gaiffas, Guillaume Lecue

Learning Latent Tree Graphical Models

Myung Jin Choi, Vincent Y.F. Tan, Animashree Anandkumar, Alan S. Willsky

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

Stephane Ross, Joelle Pineau, Brahim Chaib-draa, Pierre Kreitmann

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

Chiwoo Park, Jianhua Z. Huang, Yu Ding

-Armed Bandits

Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári

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

Vincent Y.F. Tan, Animashree Anandkumar, Alan S. Willsky

Double Updating Online Learning

Peilin Zhao, Steven C.H. Hoi, Rong Jin

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

Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama

Learning from Partial Labels

Timothee Cour, Ben Sapp, Ben Taskar

Computationally Efficient Convolved Multiple Output Gaussian Processes

Mauricio A. Álvarez, Neil D. Lawrence

Learning a Robust Relevance Model for Search Using Kernel Methods

Wei Wu, Jun Xu, Hang Li, Satoshi Oyama

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

Clustering Algorithms for Chains

Antti Ukkonen

Faster Algorithms for Max-Product Message-Passing

Julian J. Mcauley, Tibério S. Caetano

A Family of Simple Non-Parametric Kernel Learning Algorithms

Jinfeng Zhuang, Ivor W. Tsang, Steven C.H. Hoi

Better Algorithms for Benign Bandits

Elad Hazan, Satyen Kale

Locally Defined Principal Curves and Surfaces

Umut Ozertem, Deniz Erdogmus

DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model

Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara

The Indian Buffet Process: An Introduction and Review

Thomas L. Griffiths, Zoubin Ghahramani

Laplacian Support Vector Machines Trained in the Primal

Stefano Melacci, Mikhail Belkin

Anechoic Blind Source Separation Using Wigner Marginals

Lars Omlor, Martin A. Giese

Differentially Private Empirical Risk Minimization

Kamalika Chaudhuri, Claire Monteleoni, Anand D. Sarwate

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

Graham W. Taylor, Geoffrey E. Hinton, Sam T. Roweis

Zeeshan Syed, John Guttag

-Norm Multiple Kernel Learning

Marius Kloft, Ulf Brefeld, Sören Sonnenburg, Alexander Zien

Forest Density Estimation

Han Liu, Min Xu, Haijie Gu, Anupam Gupta, John Lafferty

Sparse Linear Identifiable Multivariate Modeling

Ricardo Henao, Ole Winther

Learning Transformation Models for Ranking and Survival Analysis

Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel

Information, Divergence and Risk for Binary Experiments

Mark D. Reid, Robert C. Williamson

Inverse Reinforcement Learning in Partially Observable Environments

Jaedeug Choi, Kee-eung Kim

Efficient Structure Learning of Bayesian Networks using Constraints

Cassio P. de Campos, Qiang Ji

Parameter Screening and Optimisation for ILP using Designed Experiments

Ashwin Srinivasan, Ganesh Ramakrishnan

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

Gilles Meyer, Silvère Bonnabel, Rodolphe Sepulchre

Variable Sparsity Kernel Learning

Jonathan Aflalo, Aharon Ben-tal, Chiranjib Bhattacharyya, Jagarlapudi Saketha Nath, Sankaran Raman

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

Paramveer S. Dhillon, Dean Foster, Lyle H. Ungar

Learning Multi-modal Similarity

Brian McFee, Gert Lanckriet

Posterior Sparsity in Unsupervised Dependency Parsing

Jennifer Gillenwater, Kuzman Ganchev, Jo&#227

Approximate Marginals in Latent Gaussian Models

Botond Cseke, Tom Heskes

Operator Norm Convergence of Spectral Clustering on Level Sets

Bruno Pelletier, Pierre Pudlo

Models of Cooperative Teaching and Learning

Sandra Zilles, Steffen Lange, Robert Holte, Martin Zinkevich

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

Jim C. Huang, Brendan J. Frey

A Bayesian Approximation Method for Online Ranking

Ruby C. Weng, Chih-Jen Lin

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

Vladimir V. V'yugin

Logistic Stick-Breaking Process

Lu Ren, Lan Du, Lawrence Carin, David Dunson

Training SVMs Without Offset

Ingo Steinwart, Don Hush, Clint Scovel