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JMLR Workshop and Conference Proceedings
Volume 9: AISTATS 2010

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

Editors: Yee Whye Teh and Mike Titterington
Preface
Yee Whye Teh and Mike Titterington
[pdf]

Learning the Structure of Deep Sparse Graphical Models
Ryan Adams, Hanna Wallach, Zoubin Ghahramani ; 9:1-8, 2010.
[abs] [pdf] [supplementary]

Optimal Allocation Strategies for the Dark Pool Problem
Alekh Agarwal, Peter Bartlett, Max Dama ; 9:9-16, 2010.
[abs] [pdf]

Multitask Learning for Brain-Computer Interfaces
Morteza Alamgir, Moritz Grosse–Wentrup, Yasemin Altun ; 9:17-24, 2010.
[abs] [pdf]

Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
Mauricio Álvarez, David Luengo, Michalis Titsias, Neil Lawrence ; 9:25-32, 2010.
[abs] [pdf]

Learning with Blocks: Composite Likelihood and Contrastive Divergence
Arthur Asuncion, Qiang Liu, Alexander Ihler, Padhraic Smyth ; 9:33-40, 2010.
[abs] [pdf]

Deterministic Bayesian inference for the p* model
Haakon Austad, Nial Friel ; 9:41-48, 2010.
[abs] [pdf]

Half Transductive Ranking
Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Corinna Cortes, Mehryar Mohri ; 9:49-56, 2010.
[abs] [pdf]

Kernel Partial Least Squares is Universally Consistent
Gilles Blanchard, Nicole Krämer ; 9:57-64, 2010.
[abs] [pdf]

Towards Understanding Situated Natural Language
Antoine Bordes, Nicolas Usunier, Ronan Collobert, Jason Weston ; 9:65-72, 2010.
[abs] [pdf]

Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs
Hei Chan, Manabu Kuroki ; 9:73-80, 2010.
[abs] [pdf]

Why are DBNs sparse?
Shaunak Chatterjee, Stuart Russell ; 9:81-88, 2010.
[abs] [pdf]

Focused Belief Propagation for Query-Specific Inference
Anton Chechetka, Carlos Guestrin ; 9:89-96, 2010.
[abs] [pdf]

Parametric Herding
Yutian Chen, Max Welling ; 9:97-104, 2010.
[abs] [pdf]

Mass Fatality Incident Identification based on nuclear DNA evidence
Fabio Corradi ; 9:105-112, 2010.
[abs] [pdf]

On the Impact of Kernel Approximation on Learning Accuracy
Corinna Cortes, Mehryar Mohri, Ameet Talwalkar ; 9:113-120, 2010.
[abs] [pdf]

Improving posterior marginal approximations in latent Gaussian models
Botond Cseke, Tom Heskes ; 9:121-128, 2010.
[abs] [pdf]

Impossibility Theorems for Domain Adaptation
Shai Ben David, Tyler Lu, Teresa Luu, David Pal ; 9:129-136, 2010.
[abs] [pdf]

Multiclass-Multilabel Classification with More Classes than Examples
Ofer Dekel, Ohad Shamir ; 9:137-144, 2010.
[abs] [pdf]

Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines
Guillaume Desjardins, Aaron Courville, Yoshua Bengio, Pascal Vincent, Olivier Delalleau ; 9:145-152, 2010.
[abs] [pdf]

Feature Selection using Multiple Streams
Paramveer Dhillon, Dean Foster, Lyle Ungar ; 9:153-160, 2010.
[abs] [pdf]

Bayesian variable order Markov models
Christos Dimitrakakis ; 9:161-168, 2010.
[abs] [pdf] [supplementary]

Nonparametric Bayesian Matrix Factorization by Power-EP
Nan Ding, Yuan Qi, Rongjing Xiang, Ian Molloy, Ninghui Li ; 9:169-176, 2010.
[abs] [pdf]

Neural conditional random fields
Trinh–Minh–Tri Do, Thierry Artieres ; 9:177-184, 2010.
[abs] [pdf]

Combining Experiments to Discover Linear Cyclic Models with Latent Variables
Frederick Eberhardt, Patrik Hoyer, Richard Scheines ; 9:185-192, 2010.
[abs] [pdf]

Graphical Gaussian modelling of multivariate time series with latent variables
Michael Eichler ; 9:193-200, 2010.
[abs] [pdf]

Why Does Unsupervised Pre-training Help Deep Learning?
Dumitru Erhan, Aaron Courville, Yoshua Bengio, Pascal Vincent ; 9:201-208, 2010.
[abs] [pdf]

Semi-Supervised Learning via Generalized Maximum Entropy
Ayse Erkan, Yasemin Altun ; 9:209-216, 2010.
[abs] [pdf]

Model-Free Monte Carlo-like Policy Evaluation
Raphael Fonteneau, Susan Murphy, Louis Wehenkel, Damien Ernst ; 9:217-224, 2010.
[abs] [pdf]

A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation
Florence Forbes, Senan Doyle, Daniel Garcia–Lorenzo, Christian Barillot, Michel Dojat ; 9:225-232, 2010.
[abs] [pdf]

Posterior distributions are computable from predictive distributions
Cameron Freer, Daniel Roy ; 9:233-240, 2010.
[abs] [pdf]

Variational methods for Reinforcement Learning
Thomas Furmston, David Barber ; 9:241-248, 2010.
[abs] [pdf]

Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot, Yoshua Bengio ; 9:249-256, 2010.
[abs] [pdf]

On Combining Graph-based Variance Reduction schemes
Vibhav Gogate, Rina Dechter ; 9:257-264, 2010.
[abs] [pdf]

Locally Linear Denoising on Image Manifolds
Dian Gong, Fei Sha, Gérard Medioni ; 9:265-272, 2010.
[abs] [pdf]

Regret Bounds for Gaussian Process Bandit Problems
Steffen Grünewälder, Jean–Yves Audibert, Manfred Opper, John Shawe–Taylor ; 9:273-280, 2010.
[abs] [pdf]

Sufficient covariates and linear propensity analysis
Hui Guo, Philip Dawid ; 9:281-288, 2010.
[abs] [pdf]

Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries
Shengbo Guo, Scott Sanner ; 9:289-296, 2010.
[abs] [pdf]

Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann, Aapo Hyvärinen ; 9:297-304, 2010.
[abs] [pdf]

Boosted Optimization for Network Classification
Timothy Hancock, Hiroshi Mamitsuka ; 9:305-312, 2010.
[abs] [pdf]

Dirichlet Process Mixtures of Generalized Linear Models
Lauren Hannah, David Blei, Warren Powell ; 9:313-320, 2010.
[abs] [pdf]

Negative Results for Active Learning with Convex Losses
Steve Hanneke, Liu Yang ; 9:321-325, 2010.
[abs] [pdf]

Coherent Inference on Optimal Play in Game Trees
Philipp Hennig, David Stern, Thore Graepel ; 9:326-333, 2010.
[abs] [pdf]

Collaborative Filtering via Rating Concentration
Bert Huang, Tony Jebara ; 9:334-341, 2010.
[abs] [pdf]

Maximum-likelihood learning of cumulative distribution functions on graphs
Jim Huang, Nebojsa Jojic ; 9:342-349, 2010.
[abs] [pdf]

Learning Nonlinear Dynamic Models from Non-sequenced Data
Tzu–Kuo Huang, Le Song, Jeff Schneider ; 9:350-357, 2010.
[abs] [pdf]

Learning Bayesian Network Structure using LP Relaxations
Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila ; 9:358-365, 2010.
[abs] [pdf]

Structured Sparse Principal Component Analysis
Rodolphe Jenatton, Guillaume Obozinski, Francis Bach ; 9:366-373, 2010.
[abs] [pdf]

Nonlinear functional regression: a functional RKHS approach
Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Manuel Davy ; 9:374-380, 2010.
[abs] [pdf]

Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity
Sham Kakade, Ohad Shamir, Karthik Sindharan, Ambuj Tewari ; 9:381-388, 2010.
[abs] [pdf] [supplementary]

Collaborative Filtering on a Budget
Alexandros Karatzoglou, Alex Smola, Markus Weimer ; 9:389-396, 2010.
[abs] [pdf]

Fast Active-set-type Algorithms for L1-regularized Linear Regression
Jingu Kim, Haesun Park ; 9:397-404, 2010.
[abs] [pdf]

Online Anomaly Detection under Adversarial Impact
Marius Kloft, Pavel Laskov ; 9:405-412, 2010.
[abs] [pdf] [supplementary]

Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach
Mladen Kolar, Eric Xing ; 9:413-420, 2010.
[abs] [pdf] [supplementary]

Semi-Supervised Learning with Max-Margin Graph Cuts
Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang ; 9:421-428, 2010.
[abs] [pdf]

Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms
Nevena Lazic, Brendan Frey, Parham Aarabi ; 9:429-436, 2010.
[abs] [pdf]

Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction
Guy Lever ; 9:437-444, 2010.
[abs] [pdf] [supplementary]

The Feature Selection Path in Kernel Methods
Fuxin Li, Cristian Sminchisescu ; 9:445-452, 2010.
[abs] [pdf]

Simple Exponential Family PCA
Jun Li, Dacheng Tao ; 9:453-460, 2010.
[abs] [pdf]

The Group Dantzig Selector
Han Liu, Jian Zhang, Xiaoye Jiang, Jun Liu ; 9:461-468, 2010.
[abs] [pdf]

Descent Methods for Tuning Parameter Refinement
Alexander Lorbert, Peter Ramadge ; 9:469-476, 2010.
[abs] [pdf]

Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net
Alexander Lorbert, David Eis, Victoria Kostina, David Blei, Peter Ramadge ; 9:477-484, 2010.
[abs] [pdf]

Contextual Multi-Armed Bandits
Tyler Lu, David Pal, Martin Pal ; 9:485-492, 2010.
[abs] [pdf] [supplementary]

Exploiting Feature Covariance in High-Dimensional Online Learning
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence Saul, Fernando Pereira ; 9:493-500, 2010.
[abs] [pdf]

Supervised Dimension Reduction Using Bayesian Mixture Modeling
Kai Mao, Feng Liang, Sayan Mukherjee ; 9:501-508, 2010.
[abs] [pdf]

Inductive Principles for Restricted Boltzmann Machine Learning
Benjamin Marlin, Kevin Swersky, Bo Chen, Nando de Freitas ; 9:509-516, 2010.
[abs] [pdf]

Parallelizable Sampling of Markov Random Fields
James Martens, Ilya Sutskever ; 9:517-524, 2010.
[abs] [pdf]

Exploiting Within-Clique Factorizations in Junction-Tree Algorithms
Julian McAuley, Tiberio Caetano ; 9:525-532, 2010.
[abs] [pdf]

Discriminative Topic Segmentation of Text and Speech
Mehryar Mohri, Pedro Moreno, Eugene Weinstein ; 9:533-540, 2010.
[abs] [pdf]

Elliptical slice sampling
Iain Murray, Ryan Adams, David MacKay ; 9:541-548, 2010.
[abs] [pdf] [supplementary]

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 ; 9:549-556, 2010.
[abs] [pdf]

Incremental Sparsification for Real-time Online Model Learning
Duy Nguyen–Tuong, Jan Peters ; 9:557-564, 2010.
[abs] [pdf]

Fluid Dynamics Models for Low Rank Discriminant Analysis
Yung–Kyun Noh, Byoung–Tak Zhang, Daniel Lee ; 9:565-572, 2010.
[abs] [pdf]

Approximation of hidden Markov models by mixtures of experts with application to particle filtering
Jimmy Olsson, Jonas Ströjby ; 9:573-580, 2010.
[abs] [pdf]

A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection
Silvia Pandolfi, Francesco Bartolucci, Nial Friel ; 9:581-588, 2010.
[abs] [pdf]

Bayesian structure discovery in Bayesian networks with less space
Pekka Parviainen, Mikko Koivisto ; 9:589-596, 2010.
[abs] [pdf]

Identifying Cause and Effect on Discrete Data using Additive Noise Models
Jonas Peters, Dominik Janzing, Bernhard Schölkopf ; 9:597-604, 2010.
[abs] [pdf]

REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization
Barnabas Poczos, Sergey Kirshner, Csaba Szepesvári ; 9:605-612, 2010.
[abs] [pdf]

Infinite Predictor Subspace Models for Multitask Learning
Piyush Rai, Hal Daume III ; 9:613-620, 2010.
[abs] [pdf]

Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images
Marc'Aurelio Ranzato, Alex Krizhevsky, Geoffrey Hinton ; 9:621-628, 2010.
[abs] [pdf]

Nonparametric prior for adaptive sparsity
Vikas Raykar, Linda Zhao ; 9:629-636, 2010.
[abs] [pdf]

Convexity of Proper Composite Binary Losses
Mark Reid, Robert Williamson ; 9:637-644, 2010.
[abs] [pdf]

Gaussian processes with monotonicity information
Jaakko Riihimäki, Aki Vehtari ; 9:645-652, 2010.
[abs] [pdf]

A Regularization Approach to Nonlinear Variable Selection
Lorenzo Rosasco, Matteo Santoro, Sofia Mosci, Alessandro Verri, Silvia Villa ; 9:653-660, 2010.
[abs] [pdf]

Efficient Reductions for Imitation Learning
Stephane Ross, Drew Bagnell ; 9:661-668, 2010.
[abs] [pdf] [supplementary]

Approximate parameter inference in a stochastic reaction-diffusion model
Andreas Ruttor, Manfred Opper ; 9:669-676, 2010.
[abs] [pdf]

Active Sequential Learning with Tactile Feedback
Hannes Saal, Jo–Anne Ting, Sethu Vijayakumar ; 9:677-684, 2010.
[abs] [pdf] [supplementary]

Reducing Label Complexity by Learning From Bags
Sivan Sabato, Nathan Srebro, Naftali Tishby ; 9:685-692, 2010.
[abs] [pdf]

Efficient Learning of Deep Boltzmann Machines
Ruslan Salakhutdinov, Hugo Larochelle ; 9:693-700, 2010.
[abs] [pdf]

Factorized Orthogonal Latent Spaces
Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, Trevor Darrell ; 9:701-708, 2010.
[abs] [pdf]

Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials
Mark Schmidt, Kevin Murphy ; 9:709-716, 2010.
[abs] [pdf]

Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching
Nic Schraudolph ; 9:717-724, 2010.
[abs] [pdf]

Dense Message Passing for Sparse Principal Component Analysis
Kevin Sharp, Magnus Rattray ; 9:725-732, 2010.
[abs] [pdf]

Empirical Bernstein Boosting
Pannagadatta Shivaswamy, Tony Jebara ; 9:733-740, 2010.
[abs] [pdf]

Reduced-Rank Hidden Markov Models
Sajid Siddiqi, Byron Boots, Geoffrey Gordon ; 9:741-748, 2010.
[abs] [pdf]

Detecting Weak but Hierarchically-Structured Patterns in Networks
Aarti Singh, Robert Nowak, Robert Calderbank ; 9:749-756, 2010.
[abs] [pdf]

Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks
Nikolai Slavov ; 9:757-764, 2010.
[abs] [pdf]

Nonparametric Tree Graphical Models
Le Song, Arthur Gretton, Carlos Guestrin ; 9:765-772, 2010.
[abs] [pdf] [supplementary]

On the relation between universality, characteristic kernels and RKHS embedding of measures
Bharath Sriperumbudur, Kenji Fukumizu, Gert Lanckriet ; 9:773-780, 2010.
[abs] [pdf]

Conditional Density Estimation via Least-Squares Density Ratio Estimation
Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara ; 9:781-788, 2010.
[abs] [pdf]

On the Convergence Properties of Contrastive Divergence
Ilya Sutskever, Tijmen Tieleman ; 9:789-795, 2010.
[abs] [pdf]

Inference and Learning in Networks of Queues
Charles Sutton, Michael Jordan ; 9:796-803, 2010.
[abs] [pdf]

Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation
Taiji Suzuki, Masashi Sugiyama ; 9:804-811, 2010.
[abs] [pdf]

HOP-MAP: Efficient Message Passing with High Order Potentials
Daniel Tarlow, Inmar Givoni, Richard Zemel ; 9:812-819, 2010.
[abs] [pdf]

Hartigan's Method: k-means Clustering without Voronoi
Matus Telgarsky, Andrea Vattani ; 9:820-827, 2010.
[abs] [pdf]

Learning Policy Improvements with Path Integrals
Evangelos Theodorou, Jonas Buchli, Stefan Schaal ; 9:828-835, 2010.
[abs] [pdf]

Unsupervised Aggregation for Classification Problems with Large Numbers of Categories
Ivan Titov, Alexandre Klementiev, Kevin Small, Dan Roth ; 9:836-843, 2010.
[abs] [pdf]

Bayesian Gaussian Process Latent Variable Model
Michalis Titsias, Neil Lawrence ; 9:844-851, 2010.
[abs] [pdf]

A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping
Peter Torma, András György, Csaba Szepesvári ; 9:852-859, 2010.
[abs] [pdf]

Learning Causal Structure from Overlapping Variable Sets
Sofia Triantafillou, Ioannis Tsamardinos, Ioannis Tollis ; 9:860-867, 2010.
[abs] [pdf]

State-Space Inference and Learning with Gaussian Processes
Ryan Turner, Marc Deisenroth, Carl Rasmussen ; 9:868-875, 2010.
[abs] [pdf]

Sequential Monte Carlo Samplers for Dirichlet Process Mixtures
Yener Ulker, Bilge Günsel, Taylan Cemgil ; 9:876-883, 2010.
[abs] [pdf]

Guarantees for Approximate Incremental SVMs
Nicolas Usunier, Antoine Bordes, Léon Bottou ; 9:884-891, 2010.
[abs] [pdf]

An Alternative Prior Process for Nonparametric Bayesian Clustering
Hanna Wallach, Shane Jensen, Lee Dicker, Katherine Heller ; 9:892-899, 2010.
[abs] [pdf] [supplementary]

A Potential-based Framework for Online Multi-class Learning with Partial Feedback
Shijun Wang, Rong Jin, Hamed Valizadegan ; 9:900-907, 2010.
[abs] [pdf]

Online Passive-Aggressive Algorithms on a Budget
Zhuang Wang, Slobodan Vucetic ; 9:908-915, 2010.
[abs] [pdf]

Structured Prediction Cascades
David Weiss, Benjamin Taskar ; 9:916-923, 2010.
[abs] [pdf]

Dependent Indian Buffet Processes
Sinead Williamson, Peter Orbanz, Zoubin Ghahramani ; 9:924-931, 2010.
[abs] [pdf]

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 ; 9:932-939, 2010.
[abs] [pdf]

A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra
Ji Won Yoon, Simon Wilson, K. Hun Mok ; 9:940-947, 2010.
[abs] [pdf]

Risk Bounds for Levy Processes in the PAC-Learning Framework
Chao Zhang, Dacheng Tao ; 9:948-955, 2010.
[abs] [pdf]

Bayesian Online Learning for Multi-label and Multi-variate Performance Measures
Xinhua Zhang, Thore Graepel, Ralf Herbrich ; 9:956-963, 2010.
[abs] [pdf]

Multi-Task Learning using Generalized t Process
Yu Zhang, Dit–Yan Yeung ; 9:964-971, 2010.
[abs] [pdf] [supplementary]

Bayesian Generalized Kernel Models
Zhihua Zhang, Guang Dai, Donghui Wang, Michael Jordan ; 9:972-979, 2010.
[abs] [pdf]

Matrix-Variate Dirichlet Process Mixture Models
Zhihua Zhang, Guang Dai, Michael Jordan ; 9:980-987, 2010.
[abs] [pdf]

Exclusive Lasso for Multi-task Feature Selection
Yang Zhou, Rong Jin, Steven Chu–Hong Hoi ; 9:988-995, 2010.
[abs] [pdf]


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