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Volume 2: Artificial Intelligence and Statistics, 21-24 March 2007, San Juan, Puerto Rico

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Editors: Marina Meila, Xiaotong Shen

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Contents:

Preface

Preface

Marina Meila,  Xiaotong Shen ; PMLR 2:1-2

Accepted Papers

Policy-Gradients for PSRs and POMDPs

Douglas Aberdeen,  Olivier Buffet,  Owen Thomas ; PMLR 2:3-10

Generalized Non-metric Multidimensional Scaling

Sameer Agarwal,  Josh Wills,  Lawrence Cayton,  Gert Lanckriet,  David Kriegman,  Serge Belongie ; PMLR 2:11-18

Seeking The Truly Correlated Topic Posterior - on tight approximate inference of logistic-normal admixture model

Amr Ahmed,  Eric P. Xing ; PMLR 2:19-26

A Boosting Algorithm for Label Covering in Multilabel Problems

Yonatan Amit,  Ofer Dekel,  Yoram Singer ; PMLR 2:27-34

Mixture of Watson Distributions: A Generative Model for Hyperspherical Embeddings

Avleen S. Bijral,  Markus Breitenbach,  Greg Grudic ; PMLR 2:35-42

Kernel Multi-task Learning using Task-specific Features

Edwin V. Bonilla,  Felix V. Agakov,  Christopher K. I. Williams ; PMLR 2:43-50

A Hybrid Pareto Model for Conditional Density Estimation of Asymmetric Fat-Tail Data

Julie Carreau,  Yoshua Bengio ; PMLR 2:51-58

The Laplacian Eigenmaps Latent Variable Model

Miguel A. Carreira-Perpiñán,  Zhengdong Lu ; PMLR 2:59-66

Visualizing Similarity Data with a Mixture of Maps

James Cook,  Ilya Sutskever,  Andriy Mnih,  Geoffrey Hinton ; PMLR 2:67-74

Solving Markov Random Fields with Spectral Relaxation

Timothee Cour,  Jianbo Shi ; PMLR 2:75-82

Fast search for Dirichlet process mixture models

Hal Daume III ; PMLR 2:83-90

Large-Margin Classification in Banach Spaces

Ricky Der,  Daniel Lee ; PMLR 2:91-98

Learning A* underestimates : Using inference to guide inference

Gregory Druck,  Mukund Narasimhan,  Paul Viola ; PMLR 2:99-106

Exact Bayesian structure learning from uncertain interventions

Daniel Eaton,  Kevin Murphy ; PMLR 2:107-114

Online Learning of Search Heuristics

Michael Fink ; PMLR 2:115-122

Deterministic Annealing for Multiple-Instance Learning

Peter V. Gehler,  Olivier Chapelle ; PMLR 2:123-130

Approximate inference using conditional entropy decompositions

Amir Globerson,  Tommi Jaakkola ; PMLR 2:131-138

Visualizing pairwise similarity via semidefinite programming

Amir Globerson,  Sam Roweis ; PMLR 2:139-146

SampleSearch: A Scheme that Searches for Consistent Samples

Vibhav Gogate,  Rina Dechter ; PMLR 2:147-154

Dissimilarity in Graph-Based Semi-Supervised Classification

Andrew B. Goldberg,  Xiaojin Zhu,  Stephen Wright ; PMLR 2:155-162

Hidden Topic Markov Models

Amit Gruber,  Yair Weiss,  Michal Rosen-Zvi ; PMLR 2:163-170

Space-Efficient Sampling

Sudipto Guha,  Andrew McGregor ; PMLR 2:171-178

Information Retrieval by Inferring Implicit Queries from Eye Movements

David R. Hardoon,  John Shawe-Taylor,  Antti Ajanki,  Kai Puolamäki,  Samuel Kaski ; PMLR 2:179-186

A Nonparametric Bayesian Approach to Modeling Overlapping Clusters

Katherine A. Heller,  Zoubin Ghahramani ; PMLR 2:187-194

Loopy Belief Propagation for Bipartite Maximum Weight b-Matching

Bert Huang,  Tony Jebara ; PMLR 2:195-202

Learning Markov Structure by Maximum Entropy Relaxation

Jason K. Johnson,  Venkat Chandrasekaran,  Alan S. Willsky ; PMLR 2:203-210

Multi-object tracking with representations of the symmetric group

Risi Kondor,  Andrew Howard,  Tony Jebara ; PMLR 2:211-218

MDL Histogram Density Estimation

Petri Kontkanen,  Petri Myllymäki ; PMLR 2:219-226

Incorporating Prior Knowledge on Features into Learning

Eyal Krupka,  Naftali Tishby ; PMLR 2:227-234

Fast Low-Rank Semidefinite Programming for Embedding and Clustering

Brian Kulis,  Arun C. Surendran,  John C. Platt ; PMLR 2:235-242

Learning for Larger Datasets with the Gaussian Process Latent Variable Model

Neil D. Lawrence ; PMLR 2:243-250

Learning Nearest-Neighbor Quantizers from Labeled Data by Information Loss Minimization

Svetlana Lazebnik,  Maxim Raginsky ; PMLR 2:251-258

Treelets | A Tool for Dimensionality Reduction and Multi-Scale Analysis of Unstructured Data

Ann B. Lee,  Boaz Nadler ; PMLR 2:259-266

Efficient active learning with generalized linear models

Jeremy Lewi,  Robert Butera,  Liam Paninski ; PMLR 2:267-274

A Bayesian Divergence Prior for Classiffier Adaptation

Xiao Li,  Jeff Bilmes ; PMLR 2:275-282

Sparse Nonparametric Density Estimation in High Dimensions Using the Rodeo

Han Liu,  John Lafferty,  Larry Wasserman ; PMLR 2:283-290

Fisher Consistency of Multicategory Support Vector Machines

Yufeng Liu ; PMLR 2:291-298

Semi-supervised Clustering with Pairwise Constraints: A Discriminative Approach

Zhengdong Lu ; PMLR 2:299-306

Recall Systems: Effcient Learning and Use of Category Indices

Omid Madani,  Wiley Greiner,  David Kempe,  Mohammad R. Salavatipour ; PMLR 2:307-314

AClass: A simple, online, parallelizable algorithm for probabilistic classification

Vikash K. Mansinghka,  Daniel M. Roy,  Ryan Rifkin,  Josh Tenenbaum ; PMLR 2:315-322

A Fast Bundle-based Anytime Algorithm for Poker and other Convex Games

H. Brendan McMahan,  Geoffrey J. Gordon ; PMLR 2:323-330

Loop Corrected Belief Propagation

Joris Mooij,  Bastian Wemmenhove,  Bert Kappen,  Tommaso Rizzo ; PMLR 2:331-338

Inductive Transfer for Bayesian Network Structure Learning

Alexandru Niculescu-Mizil,  Rich Caruana ; PMLR 2:339-346

Maximum Entropy Correlated Equilibria

Luis E. Ortiz,  Robert E. Schapire,  Sham M. Kakade ; PMLR 2:347-354

Approximate Counting of Graphical Models Via MCMC

Jose M. Peña ; PMLR 2:355-362

Margin based Transductive Graph Cuts using Linear Programming

K. Pelckmans,  J. Shawe-Taylor,  J.A.K. Suykens,  B. De Moor ; PMLR 2:363-370

A Unified Energy-Based Framework for Unsupervised Learning

Marc’Aurelio Ranzato,  Y-Lan Boureau,  Sumit Chopra,  Yann LeCun ; PMLR 2:371-379

(Approximate) Subgradient Methods for Structured Prediction

Nathan D. Ratliff,  J. Andrew Bagnell,  Martin A. Zinkevich ; PMLR 2:380-387

A fast algorithm for learning large scale preference relations

Vikas C. Raykar,  Ramani Duraiswami,  Balaji Krishnapuram ; PMLR 2:388-395

The Rademacher Complexity of Co-Regularized Kernel Classes

David S. Rosenberg,  Peter L. Bartlett ; PMLR 2:396-403

Continuous Neural Networks

Nicolas Le Roux,  Yoshua Bengio ; PMLR 2:404-411

Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure

Ruslan Salakhutdinov,  Geoff Hinton ; PMLR 2:412-419

A Latent Space Approach to Dynamic Embedding of Co-occurrence Data

Purnamrita Sarkar,  Sajid M. Siddiqi,  Geogrey J. Gordon ; PMLR 2:420-427

Memory-Effcient Orthogonal Least Squares Kernel Density Estimation using Enhanced Empirical Cumulative Distribution Functions

Martin Schaffoner,  Edin Andelic,  Marcel Katz,  Sven E. Krüger,  Andreas Wendemuth ; PMLR 2:428-435

A Stochastic Quasi-Newton Method for Online Convex Optimization

Nicol N. Schraudolph,  Jin Yu,  Simon Günter ; PMLR 2:436-443

Bayesian Inference and Optimal Design in the Sparse Linear Model

Matthias Seeger,  Florian Steinke,  Koji Tsuda ; PMLR 2:444-451

A Unified Algorithmic Approach for Efficient Online Label Ranking

Shai Shalev-Shwartz,  Yoram Singer ; PMLR 2:452-459

Minimum Volume Embedding

Blake Shaw,  Tony Jebara ; PMLR 2:460-467

A Framework for Probability Density Estimation

John Shawe-Taylor,  Alex Dolia ; PMLR 2:468-475

Fast Kernel ICA using an Approximate Newton Method

Hao Shen,  Stefanie Jegelka,  Arthur Gretton ; PMLR 2:476-483

Ellipsoidal Machines

Pannagadatta K. Shivaswamy,  Tony Jebara ; PMLR 2:484-491

Fast State Discovery for HMM Model Selection and Learning

Sajid M. Siddiqi,  Geogrey J. Gordon,  Andrew W. Moore ; PMLR 2:492-499

Analogical Reasoning with Relational Bayesian Sets

Ricardo Silva,  Katherine A. Heller,  Zoubin Ghahramani ; PMLR 2:500-507

Dynamic Factorization Tests: Applications to Multi-modal Data Association

Michael R. Siracusa,  John W. Fisher III ; PMLR 2:508-515

Generalized Darting Monte Carlo

Cristian Sminchisescu,  Max Welling ; PMLR 2:516-523

Local and global sparse Gaussian process approximations

Edward Snelson,  Zoubin Ghahramani ; PMLR 2:524-531

Predictive Discretization during Model Selection

Harald Steck,  Tommi S. Jaakkola ; PMLR 2:532-539

Emerge and spread models and word burstiness

Peter Sunehag ; PMLR 2:540-547

Learning Multilevel Distributed Representations for High-Dimensional Sequences

Ilya Sutskever,  Geoffrey Hinton ; PMLR 2:548-555

Stick-breaking Construction for the Indian Buffet Process

Yee Whye Teh,  Dilan Grür,  Zoubin Ghahramani ; PMLR 2:556-563

Hierarchical Beta Processes and the Indian Buffet Process

Romain Thibaux,  Michael I. Jordan ; PMLR 2:564-571

Nonlinear Dimensionality Reduction as Information Retrieval

Jarkko Venna,  Samuel Kaski ; PMLR 2:572-579

The Kernel Path in Kernelized LASSO

Gang Wang,  Dit-Yan Yeung,  Frederick H. Lochovsky ; PMLR 2:580-587

Efficient large margin semisupervised learning

Junhui Wang ; PMLR 2:588-595

Semi-Supervised Mean Fields

Fei Wang,  Shijun Wang,  Changshui Zhang,  Ole Winther ; PMLR 2:596-603

Fast Mean Shift with Accurate and Stable Convergence

Ping Wang,  Dongryeol Lee,  Alexander Gray,  James M. Rehg ; PMLR 2:604-611

Metric Learning for Kernel Regression

Kilian Q. Weinberger,  Gerald Tesauro ; PMLR 2:612-619

Performance Guarantees for Information Theoretic Active Inference

Jason L. Williams,  John W. Fisher III,  Alan S. Willsky ; PMLR 2:620-627

Transductive Classification via Local Learning Regularization

Mingrui Wu,  Bernhard Schölkopf ; PMLR 2:628-635

How Powerful Can Any Regression Learning Procedure Be?

Yuhong Yang ; PMLR 2:636-643

SVM versus Least Squares SVM

Jieping Ye,  Tao Xiong ; PMLR 2:644-651

Importance Sampling for General Hybrid Bayesian Networks

Changhe Yuan,  Marek J. Druzdzel ; PMLR 2:652-659

Nonnegative Garrote Component Selection in Functional ANOVA models

Ming Yuan ; PMLR 2:660-666

Generalized Do-Calculus with Testable Causal Assumptions

Jiji Zhang ; PMLR 2:667-674

An Improved 1-norm SVM for Simultaneous Classification and Variable Selection

Hui Zou ; PMLR 2:675-681

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