Volume 9: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 13-15 May 2010, Chia Laguna Resort, Sardinia, Italy


Editors: Yee Whye Teh, Mike Titterington




Accepted Papers


Yee Whye Teh, Mike Titterington ; PMLR 9:i-v

Learning the Structure of Deep Sparse Graphical Models

Ryan Adams, Hanna Wallach, Zoubin Ghahramani ; PMLR 9:1-8

Optimal Allocation Strategies for the Dark Pool Problem

Alekh Agarwal, Peter Bartlett, Max Dama ; PMLR 9:9-16

Multitask Learning for Brain-Computer Interfaces

Morteza Alamgir, Moritz Grosse–Wentrup, Yasemin Altun ; PMLR 9:17-24

Efficient Multioutput Gaussian Processes through Variational Inducing Kernels

Mauricio Álvarez, David Luengo, Michalis Titsias, Neil Lawrence ; PMLR 9:25-32

Learning with Blocks: Composite Likelihood and Contrastive Divergence

Arthur Asuncion, Qiang Liu, Alexander Ihler, Padhraic Smyth ; PMLR 9:33-40

Deterministic Bayesian inference for the p* model

Haakon Austad, Nial Friel ; PMLR 9:41-48

Half Transductive Ranking

Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Corinna Cortes, Mehryar Mohri ; PMLR 9:49-56

Kernel Partial Least Squares is Universally Consistent

Gilles Blanchard, Nicole Krämer ; PMLR 9:57-64

Towards Understanding Situated Natural Language

Antoine Bordes, Nicolas Usunier, Ronan Collobert, Jason Weston ; PMLR 9:65-72

Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs

Hei Chan, Manabu Kuroki ; PMLR 9:73-80

Why are DBNs sparse?

Shaunak Chatterjee, Stuart Russell ; PMLR 9:81-88

Focused Belief Propagation for Query-Specific Inference

Anton Chechetka, Carlos Guestrin ; PMLR 9:89-96

Parametric Herding

Yutian Chen, Max Welling ; PMLR 9:97-104

Mass Fatality Incident Identification based on nuclear DNA evidence

Fabio Corradi ; PMLR 9:105-112

On the Impact of Kernel Approximation on Learning Accuracy

Corinna Cortes, Mehryar Mohri, Ameet Talwalkar ; PMLR 9:113-120

Improving posterior marginal approximations in latent Gaussian models

Botond Cseke, Tom Heskes ; PMLR 9:121-128

Impossibility Theorems for Domain Adaptation

Shai Ben David, Tyler Lu, Teresa Luu, David Pal ; PMLR 9:129-136

Multiclass-Multilabel Classification with More Classes than Examples

Ofer Dekel, Ohad Shamir ; PMLR 9:137-144

Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines

Guillaume Desjardins, Aaron Courville, Yoshua Bengio, Pascal Vincent, Olivier Delalleau ; PMLR 9:145-152

Feature Selection using Multiple Streams

Paramveer Dhillon, Dean Foster, Lyle Ungar ; PMLR 9:153-160

Bayesian variable order Markov models

Christos Dimitrakakis ; PMLR 9:161-168

Nonparametric Bayesian Matrix Factorization by Power-EP

Nan Ding, Yuan Qi, Rongjing Xiang, Ian Molloy, Ninghui Li ; PMLR 9:169-176

Neural conditional random fields

Trinh–Minh–Tri Do, Thierry Artieres ; PMLR 9:177-184

Combining Experiments to Discover Linear Cyclic Models with Latent Variables

Frederick Eberhardt, Patrik Hoyer, Richard Scheines ; PMLR 9:185-192

Graphical Gaussian modelling of multivariate time series with latent variables

Michael Eichler ; PMLR 9:193-200

Why Does Unsupervised Pre-training Help Deep Learning?

Dumitru Erhan, Aaron Courville, Yoshua Bengio, Pascal Vincent ; PMLR 9:201-208

Semi-Supervised Learning via Generalized Maximum Entropy

Ayse Erkan, Yasemin Altun ; PMLR 9:209-216

Model-Free Monte Carlo-like Policy Evaluation

Raphael Fonteneau, Susan Murphy, Louis Wehenkel, Damien Ernst ; PMLR 9:217-224

A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation

Florence Forbes, Senan Doyle, Daniel Garcia–Lorenzo, Christian Barillot, Michel Dojat ; PMLR 9:225-232

Posterior distributions are computable from predictive distributions

Cameron Freer, Daniel Roy ; PMLR 9:233-240

Variational methods for Reinforcement Learning

Thomas Furmston, David Barber ; PMLR 9:241-248

Understanding the difficulty of training deep feedforward neural networks

Xavier Glorot, Yoshua Bengio ; PMLR 9:249-256

On Combining Graph-based Variance Reduction schemes

Vibhav Gogate, Rina Dechter ; PMLR 9:257-264

Locally Linear Denoising on Image Manifolds

Dian Gong, Fei Sha, Gérard Medioni ; PMLR 9:265-272

Regret Bounds for Gaussian Process Bandit Problems

Steffen Grünewälder, Jean–Yves Audibert, Manfred Opper, John Shawe–Taylor ; PMLR 9:273-280

Sufficient covariates and linear propensity analysis

Hui Guo, Philip Dawid ; PMLR 9:281-288

Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries

Shengbo Guo, Scott Sanner ; PMLR 9:289-296

Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Michael Gutmann, Aapo Hyvärinen ; PMLR 9:297-304

Boosted Optimization for Network Classification

Timothy Hancock, Hiroshi Mamitsuka ; PMLR 9:305-312

Dirichlet Process Mixtures of Generalized Linear Models

Lauren Hannah, David Blei, Warren Powell ; PMLR 9:313-320

Negative Results for Active Learning with Convex Losses

Steve Hanneke, Liu Yang ; PMLR 9:321-325

Coherent Inference on Optimal Play in Game Trees

Philipp Hennig, David Stern, Thore Graepel ; PMLR 9:326-333

Collaborative Filtering via Rating Concentration

Bert Huang, Tony Jebara ; PMLR 9:334-341

Maximum-likelihood learning of cumulative distribution functions on graphs

Jim Huang, Nebojsa Jojic ; PMLR 9:342-349

Learning Nonlinear Dynamic Models from Non-sequenced Data

Tzu–Kuo Huang, Le Song, Jeff Schneider ; PMLR 9:350-357

Learning Bayesian Network Structure using LP Relaxations

Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila ; PMLR 9:358-365

Structured Sparse Principal Component Analysis

Rodolphe Jenatton, Guillaume Obozinski, Francis Bach ; PMLR 9:366-373

Nonlinear functional regression: a functional RKHS approach

Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Manuel Davy ; PMLR 9:374-380

Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity

Sham Kakade, Ohad Shamir, Karthik Sindharan, Ambuj Tewari ; PMLR 9:381-388

Collaborative Filtering on a Budget

Alexandros Karatzoglou, Alex Smola, Markus Weimer ; PMLR 9:389-396

Fast Active-set-type Algorithms for L1-regularized Linear Regression

Jingu Kim, Haesun Park ; PMLR 9:397-404

Online Anomaly Detection under Adversarial Impact

Marius Kloft, Pavel Laskov ; PMLR 9:405-412

Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach

Mladen Kolar, Eric Xing ; PMLR 9:413-420

Semi-Supervised Learning with Max-Margin Graph Cuts

Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang ; PMLR 9:421-428

Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms

Nevena Lazic, Brendan Frey, Parham Aarabi ; PMLR 9:429-436

Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction

Guy Lever ; PMLR 9:437-444

The Feature Selection Path in Kernel Methods

Fuxin Li, Cristian Sminchisescu ; PMLR 9:445-452

Simple Exponential Family PCA

Jun Li, Dacheng Tao ; PMLR 9:453-460

The Group Dantzig Selector

Han Liu, Jian Zhang, Xiaoye Jiang, Jun Liu ; PMLR 9:461-468

Descent Methods for Tuning Parameter Refinement

Alexander Lorbert, Peter Ramadge ; PMLR 9:469-476

Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net

Alexander Lorbert, David Eis, Victoria Kostina, David Blei, Peter Ramadge ; PMLR 9:477-484

Contextual Multi-Armed Bandits

Tyler Lu, David Pal, Martin Pal ; PMLR 9:485-492

Exploiting Feature Covariance in High-Dimensional Online Learning

Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence Saul, Fernando Pereira ; PMLR 9:493-500

Supervised Dimension Reduction Using Bayesian Mixture Modeling

Kai Mao, Feng Liang, Sayan Mukherjee ; PMLR 9:501-508

Inductive Principles for Restricted Boltzmann Machine Learning

Benjamin Marlin, Kevin Swersky, Bo Chen, Nando Freitas ; PMLR 9:509-516

Parallelizable Sampling of Markov Random Fields

James Martens, Ilya Sutskever ; PMLR 9:517-524

Exploiting Within-Clique Factorizations in Junction-Tree Algorithms

Julian McAuley, Tiberio Caetano ; PMLR 9:525-532

Discriminative Topic Segmentation of Text and Speech

Mehryar Mohri, Pedro Moreno, Eugene Weinstein ; PMLR 9:533-540

Elliptical slice sampling

Iain Murray, Ryan Adams, David MacKay ; PMLR 9:541-548

Near-Optimal Evasion of Convex-Inducing Classifiers

Blaine Nelson, Benjamin Rubinstein, Ling Huang, Anthony Joseph, Shing–hon Lau, Steven Lee, Satish Rao, Anthony Tran, Doug Tygar ; PMLR 9:549-556

Incremental Sparsification for Real-time Online Model Learning

Duy Nguyen–Tuong, Jan Peters ; PMLR 9:557-564

Fluid Dynamics Models for Low Rank Discriminant Analysis

Yung–Kyun Noh, Byoung–Tak Zhang, Daniel Lee ; PMLR 9:565-572

Approximation of hidden Markov models by mixtures of experts with application to particle filtering

Jimmy Olsson, Jonas Ströjby ; PMLR 9:573-580

A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection

Silvia Pandolfi, Francesco Bartolucci, Nial Friel ; PMLR 9:581-588

Bayesian structure discovery in Bayesian networks with less space

Pekka Parviainen, Mikko Koivisto ; PMLR 9:589-596

Identifying Cause and Effect on Discrete Data using Additive Noise Models

Jonas Peters, Dominik Janzing, Bernhard Schölkopf ; PMLR 9:597-604

REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization

Barnabas Poczos, Sergey Kirshner, Csaba Szepesvári ; PMLR 9:605-612

Infinite Predictor Subspace Models for Multitask Learning

Piyush Rai, Hal Daume III ; PMLR 9:613-620

Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images

Marc’Aurelio Ranzato, Alex Krizhevsky, Geoffrey Hinton ; PMLR 9:621-628

Nonparametric prior for adaptive sparsity

Vikas Raykar, Linda Zhao ; PMLR 9:629-636

Convexity of Proper Composite Binary Losses

Mark Reid, Robert Williamson ; PMLR 9:637-644

Gaussian processes with monotonicity information

Jaakko Riihimäki, Aki Vehtari ; PMLR 9:645-652

A Regularization Approach to Nonlinear Variable Selection

Lorenzo Rosasco, Matteo Santoro, Sofia Mosci, Alessandro Verri, Silvia Villa ; PMLR 9:653-660

Efficient Reductions for Imitation Learning

Stephane Ross, Drew Bagnell ; PMLR 9:661-668

Approximate parameter inference in a stochastic reaction-diffusion model

Andreas Ruttor, Manfred Opper ; PMLR 9:669-676

Active Sequential Learning with Tactile Feedback

Hannes Saal, Jo–Anne Ting, Sethu Vijayakumar ; PMLR 9:677-684

Reducing Label Complexity by Learning From Bags

Sivan Sabato, Nathan Srebro, Naftali Tishby ; PMLR 9:685-692

Efficient Learning of Deep Boltzmann Machines

Ruslan Salakhutdinov, Hugo Larochelle ; PMLR 9:693-700

Factorized Orthogonal Latent Spaces

Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, Trevor Darrell ; PMLR 9:701-708

Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials

Mark Schmidt, Kevin Murphy ; PMLR 9:709-716

Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching

Nic Schraudolph ; PMLR 9:717-724

Dense Message Passing for Sparse Principal Component Analysis

Kevin Sharp, Magnus Rattray ; PMLR 9:725-732

Empirical Bernstein Boosting

Pannagadatta Shivaswamy, Tony Jebara ; PMLR 9:733-740

Reduced-Rank Hidden Markov Models

Sajid Siddiqi, Byron Boots, Geoffrey Gordon ; PMLR 9:741-748

Detecting Weak but Hierarchically-Structured Patterns in Networks

Aarti Singh, Robert Nowak, Robert Calderbank ; PMLR 9:749-756

Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks

Nikolai Slavov ; PMLR 9:757-764

Nonparametric Tree Graphical Models

Le Song, Arthur Gretton, Carlos Guestrin ; PMLR 9:765-772

On the relation between universality, characteristic kernels and RKHS embedding of measures

Bharath Sriperumbudur, Kenji Fukumizu, Gert Lanckriet ; PMLR 9:773-780

Conditional Density Estimation via Least-Squares Density Ratio Estimation

Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara ; PMLR 9:781-788

On the Convergence Properties of Contrastive Divergence

Ilya Sutskever, Tijmen Tieleman ; PMLR 9:789-795

Inference and Learning in Networks of Queues

Charles Sutton, Michael Jordan ; PMLR 9:796-803

Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation

Taiji Suzuki, Masashi Sugiyama ; PMLR 9:804-811

HOP-MAP: Efficient Message Passing with High Order Potentials

Daniel Tarlow, Inmar Givoni, Richard Zemel ; PMLR 9:812-819

Hartigan’s Method: k-means Clustering without Voronoi

Matus Telgarsky, Andrea Vattani ; PMLR 9:820-827

Learning Policy Improvements with Path Integrals

Evangelos Theodorou, Jonas Buchli, Stefan Schaal ; PMLR 9:828-835

Unsupervised Aggregation for Classification Problems with Large Numbers of Categories

Ivan Titov, Alexandre Klementiev, Kevin Small, Dan Roth ; PMLR 9:836-843

Bayesian Gaussian Process Latent Variable Model

Michalis Titsias, Neil Lawrence ; PMLR 9:844-851

A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping

Peter Torma, András György, Csaba Szepesvári ; PMLR 9:852-859

Learning Causal Structure from Overlapping Variable Sets

Sofia Triantafillou, Ioannis Tsamardinos, Ioannis Tollis ; PMLR 9:860-867

State-Space Inference and Learning with Gaussian Processes

Ryan Turner, Marc Deisenroth, Carl Rasmussen ; PMLR 9:868-875

Sequential Monte Carlo Samplers for Dirichlet Process Mixtures

Yener Ulker, Bilge Günsel, Taylan Cemgil ; PMLR 9:876-883

Guarantees for Approximate Incremental SVMs

Nicolas Usunier, Antoine Bordes, Léon Bottou ; PMLR 9:884-891

An Alternative Prior Process for Nonparametric Bayesian Clustering

Hanna Wallach, Shane Jensen, Lee Dicker, Katherine Heller ; PMLR 9:892-899

A Potential-based Framework for Online Multi-class Learning with Partial Feedback

Shijun Wang, Rong Jin, Hamed Valizadegan ; PMLR 9:900-907

Online Passive-Aggressive Algorithms on a Budget

Zhuang Wang, Slobodan Vucetic ; PMLR 9:908-915

Structured Prediction Cascades

David Weiss, Benjamin Taskar ; PMLR 9:916-923

Dependent Indian Buffet Processes

Sinead Williamson, Peter Orbanz, Zoubin Ghahramani ; PMLR 9:924-931

Modeling annotator expertise: Learning when everybody knows a bit of something

Yan Yan, Romer Rosales, Glenn Fung, Mark Schmidt, Gerardo Hermosillo, Luca Bogoni, Linda Moy, Jennifer Dy ; PMLR 9:932-939

A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra

Ji Won Yoon, Simon Wilson, K. Hun Mok ; PMLR 9:940-947

Risk Bounds for Levy Processes in the PAC-Learning Framework

Chao Zhang, Dacheng Tao ; PMLR 9:948-955

Bayesian Online Learning for Multi-label and Multi-variate Performance Measures

Xinhua Zhang, Thore Graepel, Ralf Herbrich ; PMLR 9:956-963

Multi-Task Learning using Generalized t Process

Yu Zhang, Dit–Yan Yeung ; PMLR 9:964-971

Bayesian Generalized Kernel Models

Zhihua Zhang, Guang Dai, Donghui Wang, Michael Jordan ; PMLR 9:972-979

Matrix-Variate Dirichlet Process Mixture Models

Zhihua Zhang, Guang Dai, Michael Jordan ; PMLR 9:980-987

Exclusive Lasso for Multi-task Feature Selection

Yang Zhou, Rong Jin, Steven Chu–Hong Hoi ; PMLR 9:988-995

subscribe via RSS

This site last compiled Mon, 10 Apr 2017 14:29:35 +0000
Github Account Copyright © PMLR 2017. All rights reserved.