Clusterability: A Theoretical Study

Margareta Ackerman,  Shai Ben-David ; PMLR 5:1-8

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Latent Force Models

Mauricio Álvarez,  David Luengo,  Neil D. Lawrence ; PMLR 5:9-16

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Variational Bridge Regression

Artin Armagan ; PMLR 5:17-24

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Learning Low Density Separators

Shai Ben-David,  Tyler Lu,  David Pal,  Miroslava Sotakova ; PMLR 5:25-32

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Supervised Spectral Latent Variable Models

Liefeng Bo,  Cristian Sminchisescu ; PMLR 5:33-40

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Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming

Hector Corrada Bravo,  Stephen Wright,  Kevin Eng,  Sunduz Keles,  Grace Wahba ; PMLR 5:41-48

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A New Perspective for Information Theoretic Feature Selection

Gavin Brown ; PMLR 5:49-56

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Structure Identification by Optimized Interventions

Alberto Giovanni Busetto,  Joachim Buhmann ; PMLR 5:57-64

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Online Inference of Topics with Latent Dirichlet Allocation

Kevin Canini,  Lei Shi,  Thomas Griffiths ; PMLR 5:65-72

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Handling Sparsity via the Horseshoe

Carlos M. Carvalho,  Nicholas G. Polson,  James G. Scott ; PMLR 5:73-80

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Relational Topic Models for Document Networks

Jonathan Chang,  David Blei ; PMLR 5:81-88

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Probabilistic Models for Incomplete Multi-dimensional Arrays

Wei Chu,  Zoubin Ghahramani ; PMLR 5:89-96

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On Partitioning Rules for Bipartite Ranking

Stephan Clemencon,  Nicolas Vayatis ; PMLR 5:97-104

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Gaussian Margin Machines

Koby Crammer,  Mehryar Mohri,  Fernando Pereira ; PMLR 5:105-112

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Learning Thin Junction Trees via Graph Cuts

Shahaf Dafna,  Carlos Guestrin ; PMLR 5:113-120

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Matching Pursuit Kernel Fisher Discriminant Analysis

Tom Diethe,  Zakria Hussain,  David Hardoon,  John Shawe-Taylor ; PMLR 5:121-128

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Statistical and Computational Tradeoffs in Stochastic Composite Likelihood

Joshua Dillon,  Guy Lebanon ; PMLR 5:129-136

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Variational Inference for the Indian Buffet Process

Finale Doshi,  Kurt Miller,  Jurgen Van Gael,  Yee Whye Teh ; PMLR 5:137-144

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Choosing a Variable to Clamp

Frederik Eaton,  Zoubin Ghahramani ; PMLR 5:145-152

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The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training

Dumitru Erhan,  Pierre-Antoine Manzagol,  Yoshua Bengio,  Samy Bengio,  Pascal Vincent ; PMLR 5:153-160

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Semi-Supervised Affinity Propagation with Instance-Level Constraints

Inmar Givoni,  Brendan Frey ; PMLR 5:161-168

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Multi-Manifold Semi-Supervised Learning

Andrew Goldberg,  Xiaojin Zhu,  Aarti Singh,  Zhiting Xu,  Robert Nowak ; PMLR 5:169-176

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Residual Splash for Optimally Parallelizing Belief Propagation

Joseph Gonzalez,  Yucheng Low,  Carlos Guestrin ; PMLR 5:177-184

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Sparse Probabilistic Principal Component Analysis

Yue Guan,  Jennifer Dy ; PMLR 5:185-192

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Visualization Databases for the Analysis of Large Complex Datasets

Saptarshi Guha,  Paul Kidwell,  Ryan P. Hafen,  William S. Cleveland ; PMLR 5:193-200

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Active Learning as Non-Convex Optimization

Andrew Guillory,  Erick Chastain,  Jeff Bilmes ; PMLR 5:201-208

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Network Completion and Survey Sampling

Steve Hanneke,  Eric P. Xing ; PMLR 5:209-215

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Distilled sensing: selective sampling for sparse signal recovery

Jarvis Haupt,  Rui Castro,  Robert Nowak ; PMLR 5:216-223

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Infinite Hierarchical Hidden Markov Models

Katherine Heller,  Yee Whye Teh,  Dilan Gorur ; PMLR 5:224-231

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An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward

Matthew Hoffman,  Nando Freitas,  Arnaud Doucet,  Jan Peters ; PMLR 5:232-239

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Maximum Entropy Density Estimation with Incomplete Presence-Only Data

Bert Huang,  Ansaf Salleb-Aouissi ; PMLR 5:240-247

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Exploiting Probabilistic Independence for Permutations

Jonathan Huang,  Carlos Guestrin,  Xiaoye Jiang,  Leonidas Guibas ; PMLR 5:248-255

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Particle Belief Propagation

Alexander Ihler,  David McAllester ; PMLR 5:256-263

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Data Biased Robust Counter Strategies

Michael Johanson,  Michael Bowling ; PMLR 5:264-271

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Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards

Varun Kanade,  H. Brendan McMahan,  Brent Bryan ; PMLR 5:272-279

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Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings

Minyoung Kim,  Vladimir Pavlovic ; PMLR 5:280-287

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Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression

Nicole Kramer,  Masashi Sugiyama,  Mikio Braun ; PMLR 5:288-295

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Convex Perturbations for Scalable Semidefinite Programming

Brian Kulis,  Suvrit Sra,  Inderjit Dhillon ; PMLR 5:296-303

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Sampling Techniques for the Nystrom Method

Sanjiv Kumar,  Mehryar Mohri,  Ameet Talwalkar ; PMLR 5:304-311

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Deep Learning using Robust Interdependent Codes

Hugo Larochelle,  Dumitru Erhan,  Pascal Vincent ; PMLR 5:312-319

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Group Nonnegative Matrix Factorization for EEG Classification

Hyekyoung Lee,  Seungjin Choi ; PMLR 5:320-327

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Kernel Learning by Unconstrained Optimization

Fuxin Li,  Yunshan Fu,  Yu-Hong Dai,  Cristian Sminchisescu,  Jue Wang ; PMLR 5:328-335

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Latent Wishart Processes for Relational Kernel Learning

Wu-Jun Li,  Zhihua Zhang,  Dit-Yan Yeung ; PMLR 5:336-343

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Tighter and Convex Maximum Margin Clustering

Yu-Feng Li,  Ivor W. Tsang,  Jame Kwok,  Zhi-Hua Zhou ; PMLR 5:344-351

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Learning Exercise Policies for American Options

Yuxi Li,  Csaba Szepesvari,  Dale Schuurmans ; PMLR 5:352-359

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Learning Sparse Markov Network Structure via Ensemble-of-Trees Models

Yuanqing Lin,  Shenghuo Zhu,  Daniel Lee,  Ben Taskar ; PMLR 5:360-367

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A kernel method for unsupervised structured network inference

Christoph Lippert,  Oliver Stegle,  Zoubin Ghahramani,  Karsten Borgwardt ; PMLR 5:368-375

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Estimation Consistency of the Group Lasso and its Applications

Han Liu,  Jian Zhang ; PMLR 5:376-383

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Learning a Parametric Embedding by Preserving Local Structure

Laurens van der Maaten ; PMLR 5:384-391

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Tractable Search for Learning Exponential Models of Rankings

Bhushan Mandhani,  Marina Meila ; PMLR 5:392-399

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Exact and Approximate Sampling by Systematic Stochastic Search

Vikash Mansinghka,  Daniel Roy,  Eric Jonas,  Joshua Tenenbaum ; PMLR 5:400-407

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Spanning Tree Approximations for Conditional Random Fields

Patrick Pletscher,  Cheng Soon Ong,  Joachim Buhmann ; PMLR 5:408-415

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Chromatic PAC-Bayes Bounds for Non-IID Data

Liva Ralaivola,  Marie Szafranski,  Guillaume Stempfel ; PMLR 5:416-423

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Inverse Optimal Heuristic Control for Imitation Learning

Nathan Ratliff,  Brian Ziebart,  Kevin Peterson,  J. Andrew Bagnell,  Martial Hebert,  Anind K. Dey,  Siddhartha Srinivasa ; PMLR 5:424-431

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Learning the Switching Rate by Discretising Bernoulli Sources Online

Steven Rooij,  Tim Erven ; PMLR 5:432-439

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Sequential Learning of Classifiers for Structured Prediction Problems

Dan Roth,  Kevin Small,  Ivan Titov ; PMLR 5:440-447

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Deep Boltzmann Machines

Ruslan Salakhutdinov,  Geoffrey Hinton ; PMLR 5:448-455

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Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm

Mark Schmidt,  Ewout Berg,  Michael Friedlander,  Kevin Murphy ; PMLR 5:456-463

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Novelty detection: Unlabeled data definitely help

Clayton Scott,  Gilles Blanchard ; PMLR 5:464-471

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PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering

Yevgeny Seldin,  Naftali Tishby ; PMLR 5:472-479

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PAC-Bayes Analysis Of Maximum Entropy Classification

John Shawe-Taylor,  David Hardoon ; PMLR 5:480-487

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Efficient graphlet kernels for large graph comparison

Nino Shervashidze,  SVN Vishwanathan,  Tobias Petri,  Kurt Mehlhorn,  Karsten Borgwardt ; PMLR 5:488-495

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Hash Kernels

Qinfeng Shi,  James Petterson,  Gideon Dror,  John Langford,  Alex Smola,  Alex Strehl,  S. V. N. Vishwanathan ; PMLR 5:496-503

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Locally Minimax Optimal Predictive Modeling with Bayesian Networks

Tomi Silander,  Teemu Roos,  Petri Myllymäki ; PMLR 5:504-511

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MCMC Methods for Bayesian Mixtures of Copulas

Ricardo Silva,  Robert Gramacy ; PMLR 5:512-519

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Factorial Mixture of Gaussians and the Marginal Independence Model

Ricardo Silva,  Zoubin Ghahramani ; PMLR 5:520-527

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Tractable Bayesian Inference of Time-Series Dependence Structure

Michael Siracusa,  John Fisher III ; PMLR 5:528-535

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Relative Novelty Detection

Alex Smola,  Le Song,  Choon Hui Teo ; PMLR 5:536-543

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Tree Block Coordinate Descent for MAP in Graphical Models

David Sontag,  Tommi Jaakkola ; PMLR 5:544-551

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The Block Diagonal Infinite Hidden Markov Model

Thomas Stepleton,  Zoubin Ghahramani,  Geoffrey Gordon,  Tai-Sing Lee ; PMLR 5:552-559

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Variable Metric Stochastic Approximation Theory

Peter Sunehag,  Jochen Trumpf,  S.V.N. Vishwanathan,  Nicol Schraudolph ; PMLR 5:560-566

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Variational Learning of Inducing Variables in Sparse Gaussian Processes

Michalis Titsias ; PMLR 5:567-574

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Non-Negative Semi-Supervised Learning

Changhu Wang,  Shuicheng Yan,  Lei Zhang,  Hongjiang Zhang ; PMLR 5:575-582

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Markov Topic Models

Chong Wang,  Bo Thiesson,  Chris Meek,  David Blei ; PMLR 5:583-590

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An Information Geometry Approach for Distance Metric Learning

Shijun Wang,  Rong Jin ; PMLR 5:591-598

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Large-Margin Structured Prediction via Linear Programming

Zhuoran Wang,  John Shawe-Taylor ; PMLR 5:599-606

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A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation

Frank Wood,  Yee Whye Teh ; PMLR 5:607-614

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Speed and Sparsity of Regularized Boosting

Yongxin Xi,  Zhen Xiang,  Peter Ramadge,  Robert Schapire ; PMLR 5:615-622

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Tree-Based Inference for Dirichlet Process Mixtures

Yang Xu,  Katherine Heller,  Zoubin Ghahramani ; PMLR 5:623-630

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Dual Temporal Difference Learning

Min Yang,  Yuxi Li,  Dale Schuurmans ; PMLR 5:631-638

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Active Sensing

Shipeng Yu,  Balaji Krishnapuram,  Romer Rosales,  R. Bharat Rao ; PMLR 5:639-646

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Coherence Functions for Multicategory Margin-based Classification Methods

Zhihua Zhang,  Michael Jordan,  Wu-Jun Li,  Dit-Yan Yeung ; PMLR 5:647-654

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Latent Variable Models for Dimensionality Reduction

Zhihua Zhang,  Michael I. Jordan ; PMLR 5:655-662

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Reversible Jump MCMC for Non-Negative Matrix Factorization

Mingjun Zhong,  Mark Girolami ; PMLR 5:663-670

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