Volume 5: Artificial Intelligence and Statistics, 16-18 April 2009, Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA


Editors: David van Dyk, Max Welling


Clusterability: A Theoretical Study

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

Latent Force Models

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

Variational Bridge Regression

Artin Armagan; PMLR 5:17-24

Learning Low Density Separators

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

Supervised Spectral Latent Variable Models

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

Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming

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

A New Perspective for Information Theoretic Feature Selection

Gavin Brown; PMLR 5:49-56

Structure Identification by Optimized Interventions

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

Online Inference of Topics with Latent Dirichlet Allocation

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

Handling Sparsity via the Horseshoe

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

Relational Topic Models for Document Networks

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

Probabilistic Models for Incomplete Multi-dimensional Arrays

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

On Partitioning Rules for Bipartite Ranking

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

Gaussian Margin Machines

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

Learning Thin Junction Trees via Graph Cuts

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

Matching Pursuit Kernel Fisher Discriminant Analysis

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

Statistical and Computational Tradeoffs in Stochastic Composite Likelihood

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

Variational Inference for the Indian Buffet Process

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

Choosing a Variable to Clamp

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

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

Semi-Supervised Affinity Propagation with Instance-Level Constraints

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

Multi-Manifold Semi-Supervised Learning

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

Residual Splash for Optimally Parallelizing Belief Propagation

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

Sparse Probabilistic Principal Component Analysis

Yue Guan, Jennifer Dy; PMLR 5:185-192

Visualization Databases for the Analysis of Large Complex Datasets

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

Active Learning as Non-Convex Optimization

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

Network Completion and Survey Sampling

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

Distilled sensing: selective sampling for sparse signal recovery

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

Infinite Hierarchical Hidden Markov Models

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

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

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

Maximum Entropy Density Estimation with Incomplete Presence-Only Data

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

Exploiting Probabilistic Independence for Permutations

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

Particle Belief Propagation

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

Data Biased Robust Counter Strategies

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

Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards

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

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

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

Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression

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

Convex Perturbations for Scalable Semidefinite Programming

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

Sampling Techniques for the Nystrom Method

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

Deep Learning using Robust Interdependent Codes

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

Group Nonnegative Matrix Factorization for EEG Classification

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

Kernel Learning by Unconstrained Optimization

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

Latent Wishart Processes for Relational Kernel Learning

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

Tighter and Convex Maximum Margin Clustering

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

Learning Exercise Policies for American Options

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

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

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

A kernel method for unsupervised structured network inference

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

Estimation Consistency of the Group Lasso and its Applications

Han Liu, Jian Zhang; PMLR 5:376-383

Learning a Parametric Embedding by Preserving Local Structure

Laurens van der Maaten; PMLR 5:384-391

Tractable Search for Learning Exponential Models of Rankings

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

Exact and Approximate Sampling by Systematic Stochastic Search

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

Spanning Tree Approximations for Conditional Random Fields

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

Chromatic PAC-Bayes Bounds for Non-IID Data

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

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

Learning the Switching Rate by Discretising Bernoulli Sources Online

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

Sequential Learning of Classifiers for Structured Prediction Problems

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

Deep Boltzmann Machines

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

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

Novelty detection: Unlabeled data definitely help

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

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

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

PAC-Bayes Analysis Of Maximum Entropy Classification

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

Efficient graphlet kernels for large graph comparison

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

Hash Kernels

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

Locally Minimax Optimal Predictive Modeling with Bayesian Networks

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

MCMC Methods for Bayesian Mixtures of Copulas

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

Factorial Mixture of Gaussians and the Marginal Independence Model

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

Tractable Bayesian Inference of Time-Series Dependence Structure

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

Relative Novelty Detection

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

Tree Block Coordinate Descent for MAP in Graphical Models

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

The Block Diagonal Infinite Hidden Markov Model

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

Variable Metric Stochastic Approximation Theory

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

Variational Learning of Inducing Variables in Sparse Gaussian Processes

Michalis Titsias; PMLR 5:567-574

Non-Negative Semi-Supervised Learning

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

Markov Topic Models

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

An Information Geometry Approach for Distance Metric Learning

Shijun Wang, Rong Jin; PMLR 5:591-598

Large-Margin Structured Prediction via Linear Programming

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

A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation

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

Speed and Sparsity of Regularized Boosting

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

Tree-Based Inference for Dirichlet Process Mixtures

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

Dual Temporal Difference Learning

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

Active Sensing

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

Coherence Functions for Multicategory Margin-based Classification Methods

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

Latent Variable Models for Dimensionality Reduction

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

Reversible Jump MCMC for Non-Negative Matrix Factorization

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

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