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All publications at Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10)
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Exclusive Lasso for Multi-task Feature Selection
Yang Zhou, Rong Jin and Steven Hoi
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Multi-Task Learning using Generalized t Process
Yu Zhang and Dit-yan Yeung
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Risk Bounds for Levy Processes in the PAC-Learning Framework
Chao Zhang and Dacheng Tao
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Bayesian Online Learning for Multi-label and Multi-variate Performance Measures
Xinhua Zhang, Thore Graepel and Ralf Herbrich
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Bayesian Generalized Kernel Models
Zhihua Zhang, Guang Dai, Donghui Wang and Michael I. Jordan
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Matrix-Variate Dirichlet Process Mixture Models
Zhihua Zhang, Guang Dai and Michael I. Jordan
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra
Ji W. Yoon, Simon P. Wilson and K. H. Mok
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Modeling annotator expertise: Learning when everybody knows a bit of something
Yan Yan, Rómer Rosales, Glenn Fung, Mark W. Schmidt, Gerardo H. Valadez, Luca Bogoni, Linda Moy and Jennifer G. Dy
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Dependent Indian Buffet Processes
Sinead Williamson, Peter Orbanz and Zoubin Ghahramani
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Structured Prediction Cascades
David Weiss and Ben Taskar
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Online Passive-Aggressive Algorithms on a Budget
Zhuang Wang and Slobodan Vucetic
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


A Potential-based Framework for Online Multi-class Learning with Partial Feedback
Shijun Wang, Rong Jin and Hamed Valizadegan
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


An Alternative Prior Process for Nonparametric Bayesian Clustering
Hanna M. Wallach, Shane Jensen, Lee Dicker and Katherine A. Heller
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Guarantees for Approximate Incremental SVMs
Nicolas Usunier, Antoine Bordes and Léon Bottou
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Sequential Monte Carlo Samplers for Dirichlet Process Mixtures
Yener Ülker, Bilge Günsel and Ali T. Cemgil
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


State-Space Inference and Learning with Gaussian Processes
Ryan D. Turner, Marc P. Deisenroth and Carl E. Rasmussen
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Learning Causal Structure from Overlapping Variable Sets
Sofia Triantafilou, Ioannis Tsamardinos and Ioannis G. Tollis
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping
Péter Torma, András György and Csaba Szepesvári
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Bayesian Gaussian Process Latent Variable Model
Michalis K. Titsias and Neil D. Lawrence
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Unsupervised Aggregation for Classification Problems with Large Numbers of Categories
Ivan Titov, Alexandre Klementiev, Kevin Small and Dan Roth
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Learning Policy Improvements with Path Integrals
Evangelos Theodorou, Jonas Buchli and Stefan Schaal
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Hartigan's Method: k-means Clustering without Voronoi
Matus Telgarsky and Andrea Vattani
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


HOP-MAP: Efficient Message Passing with High Order Potentials
Daniel Tarlow, Inmar E. Givoni and Richard S. Zemel
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Sufficient Dimension Reduction via Squared-loss Mutual Information Estimation
Taiji Suzuki and Masashi Sugiyama
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Inference and Learning in Networks of Queues
Charles A. Sutton and Michael I. Jordan
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


On the Convergence Properties of Contrastive Divergence
Ilya Sutskever and Tijmen Tieleman
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Conditional Density Estimation via Least-Squares Density Ratio Estimation
Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya and Daisuke Okanohara
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


On the relation between universality, characteristic kernels and RKHS embedding of measures
Bharath K. Sriperumbudur, Kenji Fukumizu and Gert Lanckriet
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Nonparametric Tree Graphical Models
Le Song, Arthur Gretton and Carlos Guestrin
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks
Nikolai Slavov
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Detecting Weak but Hierarchically-Structured Patterns in Networks
Aarti Singh, Robert D. Nowak and Robert Calderbank
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Reduced-Rank Hidden Markov Models
Sajid M. Siddiqi, Byron Boots and Geoffrey J. Gordon
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Empirical Bernstein Boosting
Pannagadatta K. Shivaswamy and Tony Jebara
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Dense Message Passing for Sparse Principal Component Analysis
Kevin Sharp and Magnus Rattray
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Polynomial-Time Exact Inference in NP-Hard Binary MRFs via Reweighted Perfect Matching
Nic Schraudolph
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials
Mark W. Schmidt and Kevin P. Murphy
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Factorized Orthogonal Latent Spaces
Mathieu Salzmann, Carl H. Ek, Raquel Urtasun and Trevor Darrell
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Efficient Learning of Deep Boltzmann Machines
Ruslan Salakhutdinov and Hugo Larochelle
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Reducing Label Complexity by Learning From Bags
Sivan Sabato, Nathan Srebro and Naftali Tishby
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Active Sequential Learning with Tactile Feedback
Hannes P. Saal, Jo-anne Ting and Sethu Vijayakumar
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Approximate parameter inference in a stochastic reaction-diffusion model
Andreas Ruttor and Manfred Opper
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Efficient Reductions for Imitation Learning
Stéphane Ross and Drew Bagnell
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


A Regularization Approach to Nonlinear Variable Selection
Lorenzo Rosasco, Matteo Santoro, Sofia Mosci, Alessandro Verri and Silvia Villa
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Gaussian processes with monotonicity information
Jaakko Riihimäki and Aki Vehtari
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Convexity of Proper Composite Binary Losses
Mark D. Reid and Robert C. Williamson
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Nonparametric prior for adaptive sparsity
Vikas C. Raykar and Linda H. Zhao
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images
Marc'aurelio Ranzato, Alex Krizhevsky and Geoffrey E. Hinton
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Infinite Predictor Subspace Models for Multitask Learning
Piyush Rai and Hal Daume
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization
Barnabás Póczos, Sergey Kirshner and Csaba Szepesvári
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Identifying Cause and Effect on Discrete Data using Additive Noise Models
Jonas Peters, Dominik Janzing and Bernhard Schölkopf
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Bayesian structure discovery in Bayesian networks with less space
Pekka Parviainen and Mikko Koivisto
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection
Silvia Pandolfi, Francesco Bartolucci and Nial Friel
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Approximation of hidden Markov models by mixtures of experts with application to particle filtering
Jimmy Olsson and Jonas Ströjby
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Fluid Dynamics Models for Low Rank Discriminant Analysis
Yung-kyun Noh, Byoung-tak Zhang and Daniel D. Lee
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Incremental Sparsification for Real-time Online Model Learning
Duy Nguyen-tuong and Jan R. Peters
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Near-Optimal Evasion of Convex-Inducing Classifiers
Blaine Nelson, Ling Huang, Anthony D. Joseph, Shing-hon Lau, Steven J. Lee, Satish Rao, Anthony Tran, J. D. Tygar and Benjamin I. Rubinstein
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Elliptical slice sampling
Iain Murray, Ryan P. Adams and David Mackay
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Discriminative Topic Segmentation of Text and Speech
Mehryar Mohri, Pedro Moreno and Eugene Weinstein
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Exploiting Within-Clique Factorizations in Junction-Tree Algorithms
Julian J. Mcauley and Tibério S. Caetano
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Parallelizable Sampling of Markov Random Fields
James Martens and Ilya Sutskever
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Inductive Principles for Restricted Boltzmann Machine Learning
Benjamin M. Marlin, Kevin Swersky, Bo Chen and Nando D. Freitas
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Supervised Dimension Reduction Using Bayesian Mixture Modeling
Kai Mao, Feng Liang and Sayan Mukherjee
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Exploiting Feature Covariance in High-Dimensional Online Learning
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul and Fernando Pereira
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Contextual Multi-Armed Bandits
Tyler Lu, Dávid Pál and Martin Pal
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Descent Methods for Tuning Parameter Refinement
Alexander Lorbert and Peter J. Ramadge
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net
Alexander Lorbert, David Eis, Victoria Kostina, David M. Blei and Peter J. Ramadge
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


The Group Dantzig Selector
Han Liu, Jian Zhang, Xiaoye Jiang and Jun Liu
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Simple Exponential Family PCA
Jun Li and Dacheng Tao
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


The Feature Selection Path in Kernel Methods
Fuxin Li and Cristian Sminchisescu
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Relating Function Class Complexity and Cluster Structure in the Function Domain with Applications to Transduction
Guy Lever
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Solving the Uncapacitated Facility Location Problem Using Message Passing Algorithms
Nevena Lazic, Brendan J. Frey and Parham Aarabi
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Semi-Supervised Learning with Max-Margin Graph Cuts
Branislav Kveton, Michal Valko, Ali Rahimi and Ling Huang
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: Screening Approach
Mladen Kolar and Eric P. Xing
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Online Anomaly Detection under Adversarial Impact
Marius Kloft and Pavel Laskov
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Fast Active-set-type Algorithms for L1-regularized Linear Regression
Jingu Kim and Haesun Park
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Collaborative Filtering on a Budget
Alexandros Karatzoglou, Markus Weimer and Alex J. Smola
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity
Sham Kakade, Ohad Shamir, Karthik Sindharan and Ambuj Tewari
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Nonlinear functional regression: a functional RKHS approach
Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu and Manuel Davy
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Structured Sparse Principal Component Analysis
Rodolphe Jenatton, Guillaume Obozinski and Francis R. Bach
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Learning Bayesian Network Structure using LP Relaxations
Tommi Jaakkola, David Sontag, Amir Globerson and Marina Meila
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Learning Nonlinear Dynamic Models from Non-sequenced Data
Tzu-kuo Huang, Le Song and Jeff G. Schneider
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Maximum-likelihood learning of cumulative distribution functions on graphs
Jim C. Huang and Nebojsa Jojic
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Collaborative Filtering via Rating Concentration
Bert C. Huang and Tony Jebara
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Coherent Inference on Optimal Play in Game Trees
Philipp Hennig, David H. Stern and Thore Graepel
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Negative Results for Active Learning with Convex Losses
Steve Hanneke and Liu Yang
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Dirichlet Process Mixtures of Generalized Linear Models
Lauren Hannah, David M. Blei and Warren B. Powell
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Boosted Optimization for Network Classification
Timothy Hancock and Hiroshi Mamitsuka
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries
Shengbo Guo and Scott Sanner
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Sufficient covariates and linear propensity analysis
Hui Guo and A. P. Dawid
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Regret Bounds for Gaussian Process Bandit Problems
Steffen Grünewälder, Jean-yves Audibert, Manfred Opper and John Shawe-taylor
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Locally Linear Denoising on Image Manifolds
Dian Gong, Fei Sha and Gérard G. Medioni
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


On Combining Graph-based Variance Reduction schemes
Vibhav Gogate and Rina Dechter
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Variational methods for Reinforcement Learning
Thomas Furmston and David Barber
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Posterior distributions are computable from predictive distributions
Cameron E. Freer and Daniel M. Roy
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


A Weighted Multi-Sequence Markov Model For Brain Lesion Segmentation
Florence Forbes, Senan Doyle, Daniel Garc\'ıa-lorenzo, Christian Barillot and Michel Dojat
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Model-Free Monte Carlo-like Policy Evaluation
Raphael Fonteneau, Susan A. Murphy, Louis Wehenkel and Damien Ernst
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Semi-Supervised Learning via Generalized Maximum Entropy
Ayse Erkan and Yasemin Altun
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Why Does Unsupervised Pre-training Help Deep Learning?
Dumitru Erhan, Aaron C. Courville, Yoshua Bengio and Pascal Vincent
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Graphical Gaussian modelling of multivariate time series with latent variables
Michael Eichler
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Combining Experiments to Discover Linear Cyclic Models with Latent Variables
Frederick Eberhardt, Patrik O. Hoyer and Richard Scheines
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Neural conditional random fields
Trinh Do and Thierry Arti\`eres
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Nonparametric Bayesian Matrix Factorization by Power-EP
Nan Ding, Yuan (. Qi, Rongjing Xiang, Ian Molloy and Ninghui Li
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Bayesian variable order Markov models
Christos Dimitrakakis
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Feature Selection using Multiple Streams
Paramveer S. Dhillon, Dean P. Foster and Lyle H. Ungar
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines
Guillaume Desjardins, Aaron C. Courville, Yoshua Bengio, Pascal Vincent and Olivier Delalleau
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Multiclass-Multilabel Classification with More Classes than Examples
Ofer Dekel and Ohad Shamir
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Improving posterior marginal approximations in latent Gaussian models
Botond Cseke and Tom Heskes
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


On the Impact of Kernel Approximation on Learning Accuracy
Corinna Cortes, Mehryar Mohri and Ameet Talwalkar
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Mass Fatality Incident Identification based on nuclear DNA evidence
Fabio Corradi
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Parametric Herding
Yutian Chen and Max Welling
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Focused Belief Propagation for Query-Specific Inference
Anton Chechetka and Carlos Guestrin
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Why are DBNs sparse?
Shaunak Chatterjee and Stuart Russell
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs
Hei Chan and Manabu Kuroki
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Towards Understanding Situated Natural Language
Antoine Bordes, Nicolas Usunier, Ronan Collobert and Jason Weston
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Kernel Partial Least Squares is Universally Consistent
Gilles Blanchard and Nicole Krämer
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Impossibility Theorems for Domain Adaptation
Shai Ben-david, Tyler Lu, Teresa Luu and Dávid Pál
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Half Transductive Ranking
Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Corinna Cortes and Mehryar Mohri
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Deterministic Bayesian inference for the p* model
Haakon Austad and Nial Friel
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Learning with Blocks: Composite Likelihood and Contrastive Divergence
Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler and Padhraic Smyth
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Efficient Multioutput Gaussian Processes through Variational Inducing Kernels
Mauricio A. Álvarez, David Luengo, Michalis K. Titsias and Neil D. Lawrence
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Multitask Learning for Brain-Computer Interfaces
Morteza Alamgir, Moritz Grosse-wentrup and Yasemin Altun
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Optimal Allocation Strategies for the Dark Pool Problem
Alekh Agarwal, Peter L. Bartlett and Max Dama
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


Learning the Structure of Deep Sparse Graphical Models
Ryan P. Adams, Hanna M. Wallach and Zoubin Ghahramani
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010