Volume 5: Artificial Intelligence and Statistics, 16-18 April 2009, Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA
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Editors:
David van Dyk,
Max Welling
[bib][citeproc]
Clusterability: A Theoretical Study
Margareta Ackerman,
Shai Ben-David
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PMLR 5:1-8
[abs]
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Latent Force Models
Mauricio Alvarez,
David Luengo,
Neil Lawrence
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PMLR 5:9-16
[abs]
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Variational Bridge Regression
Artin Armagan
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PMLR 5:17-24
[abs]
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Learning Low Density Separators
Shai Ben-David,
Tyler Lu,
David Pal,
Miroslava Sotakova
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PMLR 5:25-32
[abs]
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Supervised Spectral Latent Variable Models
Liefeng Bo,
Cristian Sminchisescu
;
PMLR 5:33-40
[abs]
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Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming
Hector Corrada Bravo,
Stephen Wright,
Kevin Eng,
Sunduz Keles,
Grace Wahba
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PMLR 5:41-48
[abs]
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A New Perspective for Information Theoretic Feature Selection
Gavin Brown
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PMLR 5:49-56
[abs]
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Structure Identification by Optimized Interventions
Alberto Giovanni Busetto,
Joachim Buhmann
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PMLR 5:57-64
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Online Inference of Topics with Latent Dirichlet Allocation
Kevin Canini,
Lei Shi,
Thomas Griffiths
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PMLR 5:65-72
[abs]
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Handling Sparsity via the Horseshoe
Carlos M. Carvalho,
Nicholas G. Polson,
James G. Scott
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PMLR 5:73-80
[abs]
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Relational Topic Models for Document Networks
Jonathan Chang,
David Blei
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PMLR 5:81-88
[abs]
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Probabilistic Models for Incomplete Multi-dimensional Arrays
Wei Chu,
Zoubin Ghahramani
;
PMLR 5:89-96
[abs]
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On Partitioning Rules for Bipartite Ranking
Stephan Clemencon,
Nicolas Vayatis
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PMLR 5:97-104
[abs]
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Gaussian Margin Machines
Koby Crammer,
Mehryar Mohri,
Fernando Pereira
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PMLR 5:105-112
[abs]
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Learning Thin Junction Trees via Graph Cuts
Shahaf Dafna,
Carlos Guestrin
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PMLR 5:113-120
[abs]
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Matching Pursuit Kernel Fisher Discriminant Analysis
Tom Diethe,
Zakria Hussain,
David Hardoon,
John Shawe-Taylor
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PMLR 5:121-128
[abs]
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Statistical and Computational Tradeoffs in Stochastic Composite Likelihood
Joshua Dillon,
Guy Lebanon
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PMLR 5:129-136
[abs]
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Variational Inference for the Indian Buffet Process
Finale Doshi,
Kurt Miller,
Jurgen Van Gael,
Yee Whye Teh
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PMLR 5:137-144
[abs]
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Choosing a Variable to Clamp
Frederik Eaton,
Zoubin Ghahramani
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PMLR 5:145-152
[abs]
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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
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PMLR 5:153-160
[abs]
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Semi-Supervised Affinity Propagation with Instance-Level Constraints
Inmar Givoni,
Brendan Frey
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PMLR 5:161-168
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Multi-Manifold Semi-Supervised Learning
Andrew Goldberg,
Xiaojin Zhu,
Aarti Singh,
Zhiting Xu,
Robert Nowak
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PMLR 5:169-176
[abs]
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Residual Splash for Optimally Parallelizing Belief Propagation
Joseph Gonzalez,
Yucheng Low,
Carlos Guestrin
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PMLR 5:177-184
[abs]
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Sparse Probabilistic Principal Component Analysis
Yue Guan,
Jennifer Dy
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PMLR 5:185-192
[abs]
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Visualization Databases for the Analysis of Large Complex Datasets
Saptarshi Guha,
Paul Kidwell,
Ryan P. Hafen,
William S. Cleveland
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PMLR 5:193-200
[abs]
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Active Learning as Non-Convex Optimization
Andrew Guillory,
Erick Chastain,
Jeff Bilmes
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PMLR 5:201-208
[abs]
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Network Completion and Survey Sampling
Steve Hanneke,
Eric P. Xing
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PMLR 5:209-215
[abs]
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Distilled sensing: selective sampling for sparse signal recovery
Jarvis Haupt,
Rui Castro,
Robert Nowak
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PMLR 5:216-223
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Infinite Hierarchical Hidden Markov Models
Katherine Heller,
Yee Whye Teh,
Dilan Gorur
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PMLR 5:224-231
[abs]
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An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward
Matthew Hoffman,
Nando Freitas,
Arnaud Doucet,
Jan Peters
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PMLR 5:232-239
[abs]
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Maximum Entropy Density Estimation with Incomplete Presence-Only Data
Bert Huang,
Ansaf Salleb-Aouissi
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PMLR 5:240-247
[abs]
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Exploiting Probabilistic Independence for Permutations
Jonathan Huang,
Carlos Guestrin,
Xiaoye Jiang,
Leonidas Guibas
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PMLR 5:248-255
[abs]
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Particle Belief Propagation
Alexander Ihler,
David McAllester
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PMLR 5:256-263
[abs]
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Data Biased Robust Counter Strategies
Michael Johanson,
Michael Bowling
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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
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PMLR 5:272-279
[abs]
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Covariance Operator Based Dimensionality Reduction with Extension to Semi-Supervised Settings
Minyoung Kim,
Vladimir Pavlovic
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PMLR 5:280-287
[abs]
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Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression
Nicole Kramer,
Masashi Sugiyama,
Mikio Braun
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PMLR 5:288-295
[abs]
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Convex Perturbations for Scalable Semidefinite Programming
Brian Kulis,
Suvrit Sra,
Inderjit Dhillon
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PMLR 5:296-303
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Sampling Techniques for the Nystrom Method
Sanjiv Kumar,
Mehryar Mohri,
Ameet Talwalkar
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PMLR 5:304-311
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Deep Learning using Robust Interdependent Codes
Hugo Larochelle,
Dumitru Erhan,
Pascal Vincent
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PMLR 5:312-319
[abs]
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Group Nonnegative Matrix Factorization for EEG Classification
Hyekyoung Lee,
Seungjin Choi
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PMLR 5:320-327
[abs]
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Kernel Learning by Unconstrained Optimization
Fuxin Li,
Yunshan Fu,
Yu-Hong Dai,
Cristian Sminchisescu,
Jue Wang
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PMLR 5:328-335
[abs]
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Latent Wishart Processes for Relational Kernel Learning
Wu-Jun Li,
Zhihua Zhang,
Dit-Yan Yeung
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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
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PMLR 5:344-351
[abs]
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Learning Exercise Policies for American Options
Yuxi Li,
Csaba Szepesvari,
Dale Schuurmans
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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
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PMLR 5:360-367
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A kernel method for unsupervised structured network inference
Christoph Lippert,
Oliver Stegle,
Zoubin Ghahramani,
Karsten Borgwardt
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PMLR 5:368-375
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Estimation Consistency of the Group Lasso and its Applications
Han Liu,
Jian Zhang
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PMLR 5:376-383
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Learning a Parametric Embedding by Preserving Local Structure
Laurens Maaten
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PMLR 5:384-391
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Tractable Search for Learning Exponential Models of Rankings
Bhushan Mandhani,
Marina Meila
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PMLR 5:392-399
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Exact and Approximate Sampling by Systematic Stochastic Search
Vikash Mansinghka,
Daniel Roy,
Eric Jonas,
Joshua Tenenbaum
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PMLR 5:400-407
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Spanning Tree Approximations for Conditional Random Fields
Patrick Pletscher,
Cheng Soon Ong,
Joachim Buhmann
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PMLR 5:408-415
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Chromatic PAC-Bayes Bounds for Non-IID Data
Liva Ralaivola,
Marie Szafranski,
Guillaume Stempfel
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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
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PMLR 5:424-431
[abs]
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Learning the Switching Rate by Discretising Bernoulli Sources Online
Steven Rooij,
Tim Erven
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PMLR 5:432-439
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Sequential Learning of Classifiers for Structured Prediction Problems
Dan Roth,
Kevin Small,
Ivan Titov
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PMLR 5:440-447
[abs]
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Deep Boltzmann Machines
Ruslan Salakhutdinov,
Geoffrey Hinton
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PMLR 5:448-455
[abs]
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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
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PMLR 5:456-463
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Novelty detection: Unlabeled data definitely help
Clayton Scott,
Gilles Blanchard
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PMLR 5:464-471
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PAC-Bayesian Generalization Bound for Density Estimation with Application to Co-clustering
Yevgeny Seldin,
Naftali Tishby
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PMLR 5:472-479
[abs]
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PAC-Bayes Analysis Of Maximum Entropy Classification
John Shawe-Taylor,
David Hardoon
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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
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PMLR 5:488-495
[abs]
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Hash Kernels
Qinfeng Shi,
James Petterson,
Gideon Dror,
John Langford,
Alex Smola,
Alex Strehl,
Vishy Vishwanathan
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PMLR 5:496-503
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Locally Minimax Optimal Predictive Modeling with Bayesian Networks
Tomi Silander,
Teemu Roos,
Petri Myllymaki
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PMLR 5:504-511
[abs]
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MCMC Methods for Bayesian Mixtures of Copulas
Ricardo Silva,
Robert Gramacy
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PMLR 5:512-519
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Factorial Mixture of Gaussians and the Marginal Independence Model
Ricardo Silva,
Zoubin Ghahramani
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PMLR 5:520-527
[abs]
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Tractable Bayesian Inference of Time-Series Dependence Structure
Michael Siracusa,
John Fisher III
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PMLR 5:528-535
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Relative Novelty Detection
Alex Smola,
Le Song,
Choon Hui Teo
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PMLR 5:536-543
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Tree Block Coordinate Descent for MAP in Graphical Models
David Sontag,
Tommi Jaakkola
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PMLR 5:544-551
[abs]
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The Block Diagonal Infinite Hidden Markov Model
Thomas Stepleton,
Zoubin Ghahramani,
Geoffrey Gordon,
Tai-Sing Lee
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PMLR 5:552-559
[abs]
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Variable Metric Stochastic Approximation Theory
Peter Sunehag,
Jochen Trumpf,
S.V.N. Vishwanathan,
Nicol Schraudolph
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PMLR 5:560-566
[abs]
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Variational Learning of Inducing Variables in Sparse Gaussian Processes
Michalis Titsias
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PMLR 5:567-574
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Non-Negative Semi-Supervised Learning
Changhu Wang,
Shuicheng Yan,
Lei Zhang,
Hongjiang Zhang
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PMLR 5:575-582
[abs]
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Markov Topic Models
Chong Wang,
Bo Thiesson,
Chris Meek,
David Blei
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PMLR 5:583-590
[abs]
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An Information Geometry Approach for Distance Metric Learning
Shijun Wang,
Rong Jin
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PMLR 5:591-598
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Large-Margin Structured Prediction via Linear Programming
Zhuoran Wang,
John Shawe-Taylor
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PMLR 5:599-606
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A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation
Frank Wood,
Yee Whye Teh
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PMLR 5:607-614
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Speed and Sparsity of Regularized Boosting
Yongxin Xi,
Zhen Xiang,
Peter Ramadge,
Robert Schapire
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PMLR 5:615-622
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Tree-Based Inference for Dirichlet Process Mixtures
Yang Xu,
Katherine Heller,
Zoubin Ghahramani
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PMLR 5:623-630
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Dual Temporal Difference Learning
Min Yang,
Yuxi Li,
Dale Schuurmans
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PMLR 5:631-638
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Active Sensing
Shipeng Yu,
Balaji Krishnapuram,
Romer Rosales,
R. Bharat Rao
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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
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PMLR 5:647-654
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Latent Variable Models for Dimensionality Reduction
Zhihua Zhang,
Michael Jordan
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PMLR 5:655-662
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Reversible Jump MCMC for Non-Negative Matrix Factorization
Mingjun Zhong,
Mark Girolami
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PMLR 5:663-670
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