AISTATS 2005

84 papers

Reversible Jump MCMC for Non-Negative Matrix Factorization

Mingjun Zhong, Mark Girolami

Latent Variable Models for Dimensionality Reduction

Zhihua Zhang, Michael I. Jordan

Coherence Functions for Multicategory Margin-based Classification Methods

Zhihua Zhang, Michael Jordan, Wu-jun Li, Dit-Yan Yeung

Active Sensing

Shipeng Yu, Balaji Krishnapuram, Rómer Rosales, R. Bharat Rao

Dual Temporal Difference Learning

Min Yang, Yuxi Li, Dale Schuurmans

Tree-Based Inference for Dirichlet Process Mixtures

Yang Xu, Katherine Heller, Zoubin Ghahramani

Speed and Sparsity of Regularized Boosting

Yongxin Xi, Zhen Xiang, Peter Ramadge, Robert Schapire

A Hierarchical Nonparametric Bayesian Approach to Statistical Language Model Domain Adaptation

Frank Wood, Yee Whye Teh

Large-Margin Structured Prediction via Linear Programming

Zhuoran Wang, John Shawe-Taylor

An Information Geometry Approach for Distance Metric Learning

Shijun Wang, Rong Jin

Markov Topic Models

Chong Wang, Bo Thiesson, Chris Meek, David Blei

Non-Negative Semi-Supervised Learning

Changhu Wang, Shuicheng Yan, Lei Zhang, Hongjiang Zhang

Variational Learning of Inducing Variables in Sparse Gaussian Processes

Michalis Titsias

Variable Metric Stochastic Approximation Theory

Peter Sunehag, Jochen Trumpf, S.v.n. Vishwanathan, Nicol Schraudolph

The Block Diagonal Infinite Hidden Markov Model

Thomas Stepleton, Zoubin Ghahramani, Geoffrey Gordon, Tai-Sing Lee

Tree Block Coordinate Descent for MAP in Graphical Models

David Sontag, Tommi Jaakkola

Relative Novelty Detection

Alex Smola, Le Song, Choon Hui Teo

Tractable Bayesian Inference of Time-Series Dependence Structure

Michael Siracusa, John Fisher III

Factorial Mixture of Gaussians and the Marginal Independence Model

Ricardo Silva, Zoubin Ghahramani

MCMC Methods for Bayesian Mixtures of Copulas

Ricardo Silva, Robert Gramacy

Locally Minimax Optimal Predictive Modeling with Bayesian Networks

Tomi Silander, Teemu Roos, Petri Myllymäki

Hash Kernels

Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alex Smola

Efficient graphlet kernels for large graph comparison

Nino Shervashidze, SVN Vishwanathan, Tobias Petri, Kurt Mehlhorn, Karsten Borgwardt

PAC-Bayes Analysis Of Maximum Entropy Classification

John Shawe-Taylor, David Hardoon

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

Yevgeny Seldin, Naftali Tishby

Novelty detection: Unlabeled data definitely help

Clayton Scott, Gilles Blanchard

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

Mark Schmidt, Ewout Berg, Michael Friedlander, Kevin Murphy

Deep Boltzmann Machines

Ruslan Salakhutdinov, Geoffrey Hinton

Sequential Learning of Classifiers for Structured Prediction Problems

Dan Roth, Kevin Small, Ivan Titov

Learning the Switching Rate by Discretising Bernoulli Sources Online

Steven Rooij, Tim Erven

Inverse Optimal Heuristic Control for Imitation Learning

Nathan Ratliff, Brian Ziebart, Kevin Peterson, J. Andrew Bagnell, Martial Hebert

Chromatic PAC-Bayes Bounds for Non-IID Data

Liva Ralaivola, Marie Szafranski, Guillaume Stempfel

Spanning Tree Approximations for Conditional Random Fields

Patrick Pletscher, Cheng Soon Ong, Joachim Buhmann

Exact and Approximate Sampling by Systematic Stochastic Search

Vikash Mansinghka, Daniel Roy, Eric Jonas, Joshua Tenenbaum

Tractable Search for Learning Exponential Models of Rankings

Bhushan Mandhani, Marina Meila

Learning a Parametric Embedding by Preserving Local Structure

Laurens van der Maaten

Estimation Consistency of the Group Lasso and its Applications

Han Liu, Jian Zhang

A kernel method for unsupervised structured network inference

Christoph Lippert, Oliver Stegle, Zoubin Ghahramani, Karsten Borgwardt

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

Yuanqing Lin, Shenghuo Zhu, Daniel Lee, Ben Taskar

Learning Exercise Policies for American Options

Yuxi Li, Csaba Szepesvári, Dale Schuurmans

Tighter and Convex Maximum Margin Clustering

Yu-feng Li, Ivor W. Tsang, Jame Kwok, Zhi-Hua Zhou

Latent Wishart Processes for Relational Kernel Learning

Wu-jun Li, Zhihua Zhang, Dit-Yan Yeung

Kernel Learning by Unconstrained Optimization

Fuxin Li, Yunshan Fu, Yu-Hong Dai, Cristian Sminchisescu, Jue Wang

Group Nonnegative Matrix Factorization for EEG Classification

Hyekyoung Lee, Seungjin Choi

Deep Learning using Robust Interdependent Codes

Hugo Larochelle, Dumitru Erhan, Pascal Vincent

Sampling Techniques for the Nystrom Method

Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar

Convex Perturbations for Scalable Semidefinite Programming

Brian Kulis, Suvrit Sra, Inderjit Dhillon

Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression

Nicole Krämer, Masashi Sugiyama, Mikio Braun

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

Minyoung Kim, Vladimir Pavlovic

Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards

Varun Kanade, H. Brendan McMahan, Brent Bryan

Data Biased Robust Counter Strategies

Michael Johanson, Michael Bowling

Particle Belief Propagation

Alexander Ihler, David Mcallester

Exploiting Probabilistic Independence for Permutations

Jonathan Huang, Carlos Guestrin, Xiaoye Jiang, Leonidas Guibas

Maximum Entropy Density Estimation with Incomplete Presence-Only Data

Bert Huang, Ansaf Salleb-Aouissi

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

Matthew Hoffman, Nando Freitas, Arnaud Doucet, Jan Peters

Infinite Hierarchical Hidden Markov Models

Katherine Heller, Yee Whye Teh, Dilan Gorur

Distilled sensing: selective sampling for sparse signal recovery

Jarvis Haupt, Rui Castro, Robert Nowak

Network Completion and Survey Sampling

Steve Hanneke, Eric P. Xing

Active Learning as Non-Convex Optimization

Andrew Guillory, Erick Chastain, Jeff Bilmes

Visualization Databases for the Analysis of Large Complex Datasets

Saptarshi Guha, Paul Kidwell, Ryan P. Hafen, William S. Cleveland

Sparse Probabilistic Principal Component Analysis

Yue Guan, Jennifer Dy

Residual Splash for Optimally Parallelizing Belief Propagation

Joseph Gonzalez, Yucheng Low, Carlos Guestrin

Multi-Manifold Semi-Supervised Learning

Andrew Goldberg, Xiaojin Zhu, Aarti Singh, Zhiting Xu, Robert Nowak

Semi-Supervised Affinity Propagation with Instance-Level Constraints

Inmar Givoni, Brendan Frey

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

Dumitru Erhan, Pierre-antoine Manzagol, Yoshua Bengio, Samy Bengio, Pascal Vincent

Choosing a Variable to Clamp

Frederik Eaton, Zoubin Ghahramani

Variational Inference for the Indian Buffet Process

Finale Doshi, Kurt Miller, Jurgen Van Gael, Yee Whye Teh

Statistical and Computational Tradeoffs in Stochastic Composite Likelihood

Joshua Dillon, Guy Lebanon

Matching Pursuit Kernel Fisher Discriminant Analysis

Tom Diethe, Zakria Hussain, David Hardoon, John Shawe-Taylor

Learning Thin Junction Trees via Graph Cuts

Shahaf Dafna, Carlos Guestrin

Gaussian Margin Machines

Koby Crammer, Mehryar Mohri, Fernando Pereira

On Partitioning Rules for Bipartite Ranking

Stéphan Clémençon, Nicolas Vayatis

Probabilistic Models for Incomplete Multi-dimensional Arrays

Wei Chu, Zoubin Ghahramani

Relational Topic Models for Document Networks

Jonathan Chang, David Blei

Handling Sparsity via the Horseshoe

Carlos M. Carvalho, Nicholas G. Polson, James G. Scott

Online Inference of Topics with Latent Dirichlet Allocation

Kevin Canini, Lei Shi, Thomas Griffiths

Structure Identification by Optimized Interventions

Alberto Giovanni Busetto, Joachim Buhmann

A New Perspective for Information Theoretic Feature Selection

Gavin Brown

Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming

Hector Corrada Bravo, Stephen Wright, Kevin Eng, Sunduz Keles, Grace Wahba

Supervised Spectral Latent Variable Models

Liefeng Bo, Cristian Sminchisescu

Learning Low Density Separators

Shai Ben-David, Tyler Lu, Dávid Pál, Miroslava Sotakova

Variational Bridge Regression

Artin Armagan

Latent Force Models

Mauricio Alvarez, David Luengo, Neil D. Lawrence

Clusterability: A Theoretical Study

Margareta Ackerman, Shai Ben-David