JMLR 2010

118 papers

Learning Non-Stationary Dynamic Bayesian Networks

Joshua W. Robinson, Alexander J. Hartemink

PAC-Bayesian Analysis of Co-clustering and Beyond

Yevgeny Seldin, Naftali Tishby

Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory

Sumio Watanabe

Incremental Sigmoid Belief Networks for Grammar Learning

James Henderson, Ivan Titov

Rate Minimaxity of the Lasso and Dantzig Selector for the Loss in Balls

Fei Ye, Cun-hui Zhang

An Exponential Model for Infinite Rankings

Marina Meil&#259

Efficient Algorithms for Conditional Independence Inference

Remco Bouckaert, Raymond Hemmecke, Silvia Lindner, Milan Studen&#253

-Nested Symmetric Distributions

Fabian Sinz, Matthias Bethge

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-antoine Manzagol

Learning Instance-Specific Predictive Models

Shyam Visweswaran, Gregory F. Cooper

Maximum Likelihood in Cost-Sensitive Learning: Model Specification, Approximations, and Upper Bounds

Jacek P. Dmochowski, Paul Sajda, Lucas C. Parra

Classification with Incomplete Data Using Dirichlet Process Priors

Chunping Wang, Xuejun Liao, Lawrence Carin, David B. Dunson

Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes

Antti Honkela, Tapani Raiko, Mikael Kuusela, Matti Tornio, Juha Karhunen

A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification

Guo-Xun Yuan, Kai-Wei Chang, Cho-jui Hsieh, Chih-Jen Lin

A Generalized Path Integral Control Approach to Reinforcement Learning

Evangelos Theodorou, Jonas Buchli, Stefan Schaal

Collective Inference for Extraction MRFs Coupled with Symmetric Clique Potentials

Rahul Gupta, Sunita Sarawagi, Ajit A. Diwan

Inducing Tree-Substitution Grammars

Trevor Cohn, Phil Blunsom, Sharon Goldwater

Covariance in Unsupervised Learning of Probabilistic Grammars

Shay B. Cohen, Noah A. Smith

Gaussian Processes for Machine Learning (GPML) Toolbox

Carl Edward Rasmussen, Hannes Nickisch

Semi-Supervised Novelty Detection

Gilles Blanchard, Gyemin Lee, Clayton Scott

Tree Decomposition for Large-Scale SVM Problems

Fu Chang, Chien-Yang Guo, Xiao-Rong Lin, Chi-Jen Lu

Linear Algorithms for Online Multitask Classification

Giovanni Cavallanti, Nicolò Cesa-bianchi, Claudio Gentile

Expectation Truncation and the Benefits of Preselection In Training Generative Models

Jörg Lücke, Julian Eggert

Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance

Nguyen Xuan Vinh, Julien Epps, James Bailey

Regret Bounds and Minimax Policies under Partial Monitoring

Jean-yves Audibert, Sébastien Bubeck

Mean Field Variational Approximation for Continuous-Time Bayesian Networks

Ido Cohn, Tal El-Hay, Nir Friedman, Raz Kupferman

Using Contextual Representations to Efficiently Learn Context-Free Languages

Alexander Clark, Rémi Eyraud, Amaury Habrard

Topology Selection in Graphical Models of Autoregressive Processes

Jitkomut Songsiri, Lieven Vandenberghe

Learnability, Stability and Uniform Convergence

Shai Shalev-shwartz, Ohad Shamir, Nathan Srebro, Karthik Sridharan

Stochastic Composite Likelihood

Joshua V. Dillon, Guy Lebanon

Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization

Lin Xiao

WEKA−Experiences with a Java Open-Source Project

Remco R. Bouckaert, Eibe Frank, Mark A. Hall, Geoffrey Holmes, Bernhard Pfahringer

Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data

Miloš Radovanovi&#263

Rademacher Complexities and Bounding the Excess Risk in Active Learning

Vladimir Koltchinskii

Sparse Semi-supervised Learning Using Conjugate Functions

Shiliang Sun, John Shawe-Taylor

Composite Binary Losses

Mark D. Reid, Robert C. Williamson

High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency

Dapo Omidiran, Martin J. Wainwright

Efficient Heuristics for Discriminative Structure Learning of Bayesian Network Classifiers

Franz Pernkopf, Jeff A. Bilmes

Spectral Regularization Algorithms for Learning Large Incomplete Matrices

Rahul Mazumder, Trevor Hastie, Robert Tibshirani

High Dimensional Inverse Covariance Matrix Estimation via Linear Programming

Ming Yuan

Restricted Eigenvalue Properties for Correlated Gaussian Designs

Garvesh Raskutti, Martin J. Wainwright, Bin Yu

Erratum: SGDQN is Less Careful than Expected

Antoine Bordes, Léon Bottou, Patrick Gallinari, Jonathan Chang, S. Alex Smith

Regularized Discriminant Analysis, Ridge Regression and Beyond

Zhihua Zhang, Guang Dai, Congfu Xu, Michael I. Jordan

Learning Gradients: Predictive Models that Infer Geometry and Statistical Dependence

Qiang Wu, Justin Guinney, Mauro Maggioni, Sayan Mukherjee

libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models

Joris M. Mooij

Matched Gene Selection and Committee Classifier for Molecular Classification of Heterogeneous Diseases

Guoqiang Yu, Yuanjian Feng, David J. Miller, Jianhua Xuan, Eric P. Hoffman

Importance Sampling for Continuous Time Bayesian Networks

Yu Fan, Jing Xu, Christian R. Shelton

Model-based Boosting 2.0

Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner

On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

Gavin C. Cawley, Nicola L. C. Talbot

Matrix Completion from Noisy Entries

Raghunandan H. Keshavan, Andrea Montanari, Sewoong Oh

A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design

Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom Dhaene, Karel Crombecq

Posterior Regularization for Structured Latent Variable Models

Kuzman Ganchev, Jo&#227

Practical Approaches to Principal Component Analysis in the Presence of Missing Values

Alexander Ilin, Tapani Raiko

Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes

Liva Ralaivola, Marie Szafranski, Guillaume Stempfel

Fast and Scalable Local Kernel Machines

Nicola Segata, Enrico Blanzieri

Sparse Spectrum Gaussian Process Regression

Miguel Lázaro-Gredilla, Joaquin Quiñnero-Candela, Carl Edward Rasmussen, An&#237

Permutation Tests for Studying Classifier Performance

Markus Ojala, Gemma C. Garriga

How to Explain Individual Classification Decisions

David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen

The SHOGUN Machine Learning Toolbox

Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr

Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing

Ryo Yoshida, Mike West

Evolving Static Representations for Task Transfer

Phillip Verbancsics, Kenneth O. Stanley

FastInf: An Efficient Approximate Inference Library

Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan

Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity

Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer

Consensus-Based Distributed Support Vector Machines

Pedro A. Forero, Alfonso Cano, Georgios B. Giannakis

Introduction to Causal Inference

Peter Spirtes

On the Foundations of Noise-free Selective Classification

Ran El-Yaniv, Yair Wiener

MOA: Massive Online Analysis

Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer

Near-optimal Regret Bounds for Reinforcement Learning

Thomas Jaksch, Ronald Ortner, Peter Auer

Hilbert Space Embeddings and Metrics on Probability Measures

Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf, Gert R.G. Lanckriet

Quadratic Programming Feature Selection

Irene Rodriguez-Lujan, Ramon Huerta, Charles Elkan, Carlos Santa Cruz

Training and Testing Low-degree Polynomial Data Mappings via Linear SVM

Yin-Wen Chang, Cho-jui Hsieh, Kai-Wei Chang, Michael Ringgaard, Chih-Jen Lin

Characterization, Stability and Convergence of Hierarchical Clustering Methods

Gunnar Carlsson, Facundo Mémoli

Consistent Nonparametric Tests of Independence

Arthur Gretton, László Györfi

Learning Translation Invariant Kernels for Classification

Kamaledin Ghiasi-Shirazi, Reza Safabakhsh, Mostafa Shamsi

Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels

Pinar Donmez, Guy Lebanon, Krishnakumar Balasubramanian

Learning From Crowds

Vikas C. Raykar, Shipeng Yu, Linda H. Zhao, Gerardo Hermosillo Valadez, Charles Florin

Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation

Vicenç Gómez, Hilbert J. Kappen, Michael Chertkov

Stochastic Complexity and Generalization Error of a Restricted Boltzmann Machine in Bayesian Estimation

Miki Aoyagi

Graph Kernels

S.v.n. Vishwanathan, Nicol N. Schraudolph, Risi Kondor, Karsten M. Borgwardt

A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning

Jin Yu, S.v.n. Vishwanathan, Simon Günter, Nicol N. Schraudolph

SFO: A Toolbox for Submodular Function Optimization

Andreas Krause

Continuous Time Bayesian Network Reasoning and Learning Engine

Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu

Large Scale Online Learning of Image Similarity Through Ranking

Gal Chechik, Varun Sharma, Uri Shalit, Samy Bengio

Analysis of Multi-stage Convex Relaxation for Sparse Regularization

Tong Zhang

Message-passing for Graph-structured Linear Programs: Proximal Methods and Rounding Schemes

Pradeep Ravikumar, Alekh Agarwal, Martin J. Wainwright

Kronecker Graphs: An Approach to Modeling Networks

Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, Zoubin Ghahramani

Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data

Gideon S. Mann, Andrew McCallum

On Spectral Learning

Andreas Argyriou, Charles A. Micchelli, Massimiliano Pontil

On Learning with Integral Operators

Lorenzo Rosasco, Mikhail Belkin, Ernesto De Vito

Image Denoising with Kernels Based on Natural Image Relations

Valero Laparra, Juan Gutiérrez, Gustavo Camps-Valls, Jes&#250

A Streaming Parallel Decision Tree Algorithm

Yael Ben-Haim, Elad Tom-Tov

Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models

Fang-Lan Huang, Cho-jui Hsieh, Kai-Wei Chang, Chih-Jen Lin

Stability Bounds for Stationary φ-mixing and β-mixing Processes

Mehryar Mohri, Afshin Rostamizadeh

Maximum Relative Margin and Data-Dependent Regularization

Pannagadatta K. Shivaswamy, Tony Jebara

PyBrain

Tom Schaul, Justin Bayer, Daan Wierstra, Yi Sun, Martin Felder

A Fast Hybrid Algorithm for Large-Scale -Regularized Logistic Regression

Jianing Shi, Wotao Yin, Stanley Osher, Paul Sajda

On the Rate of Convergence of the Bagged Nearest Neighbor Estimate

Gérard Biau, Frédéric Cérou, Arnaud Guyader

Second-Order Bilinear Discriminant Analysis

Christoforos Christoforou, Robert Haralick, Paul Sajda, Lucas C. Parra

Error-Correcting Output Codes Library

Sergio Escalera, Oriol Pujol, Petia Radeva

Why Does Unsupervised Pre-training Help Deep Learning?

Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-antoine Manzagol, Pascal Vincent