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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