AISTATS 2010
125 papers
A Regularization Approach to Nonlinear Variable Selection
Lorenzo Rosasco, Matteo Santoro, Sofia Mosci, Alessandro Verri, Silvia Villa A Weighted Multi-Sequence Markov Model for Brain Lesion Segmentation
Florence Forbes, Senan Doyle, Daniel Garcia–Lorenzo, Christian Barillot, Michel Dojat Bayesian Generalized Kernel Models
Zhihua Zhang, Guang Dai, Donghui Wang, Michael I. Jordan Collaborative Filtering on a Budget
Alexandros Karatzoglou, Alex Smola, Markus Weimer Conditional Density Estimation via Least-Squares Density Ratio Estimation
Masashi Sugiyama, Ichiro Takeuchi, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Daisuke Okanohara Contextual Multi-Armed Bandits
Tyler Lu, David Pal, Martin Pal Dependent Indian Buffet Processes
Sinead Williamson, Peter Orbanz, Zoubin Ghahramani Elliptical Slice Sampling
Iain Murray, Ryan Adams, David MacKay Empirical Bernstein Boosting
Pannagadatta Shivaswamy, Tony Jebara Exploiting Feature Covariance in High-Dimensional Online Learning
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence Saul, Fernando Pereira Factorized Orthogonal Latent Spaces
Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, Trevor Darrell Feature Selection Using Multiple Streams
Paramveer Dhillon, Dean Foster, Lyle Ungar Guarantees for Approximate Incremental SVMs
Nicolas Usunier, Antoine Bordes, Léon Bottou Half Transductive Ranking
Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Corinna Cortes, Mehryar Mohri Impossibility Theorems for Domain Adaptation
Shai Ben David, Tyler Lu, Teresa Luu, David Pal Learning Bayesian Network Structure Using LP Relaxations
Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila Learning Causal Structure from Overlapping Variable Sets
Sofia Triantafillou, Ioannis Tsamardinos, Ioannis Tollis Learning Policy Improvements with Path Integrals
Evangelos Theodorou, Jonas Buchli, Stefan Schaal Model-Free Monte Carlo-like Policy Evaluation
Raphael Fonteneau, Susan Murphy, Louis Wehenkel, Damien Ernst 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 Multitask Learning for Brain-Computer Interfaces
Morteza Alamgir, Moritz Grosse–Wentrup, Yasemin Altun 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 Neural Conditional Random Fields
Trinh–Minh–Tri Do, Thierry Artieres Nonlinear Functional Regression: A Functional RKHS Approach
Hachem Kadri, Emmanuel Duflos, Philippe Preux, Stéphane Canu, Manuel Davy Nonparametric Bayesian Matrix Factorization by Power-EP
Nan Ding, Yuan Qi, Rongjing Xiang, Ian Molloy, Ninghui Li Nonparametric Tree Graphical Models
Le Song, Arthur Gretton, Carlos Guestrin Parametric Herding
Yutian Chen, Max Welling Reduced-Rank Hidden Markov Models
Sajid Siddiqi, Byron Boots, Geoffrey Gordon Regret Bounds for Gaussian Process Bandit Problems
Steffen Grünewälder, Jean–Yves Audibert, Manfred Opper, John Shawe–Taylor Semi-Supervised Learning with Max-Margin Graph Cuts
Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang Structured Sparse Principal Component Analysis
Rodolphe Jenatton, Guillaume Obozinski, Francis Bach Tempered Markov Chain Monte Carlo for Training of Restricted Boltzmann Machines
Guillaume Desjardins, Aaron Courville, Yoshua Bengio, Pascal Vincent, Olivier Delalleau The Group Dantzig Selector
Han Liu, Jian Zhang, Xiaoye Jiang, Jun Liu Towards Understanding Situated Natural Language
Antoine Bordes, Nicolas Usunier, Ronan Collobert, Jason Weston Why Are DBNs Sparse?
Shaunak Chatterjee, Stuart Russell Why Does Unsupervised Pre-Training Help Deep Learning?
Dumitru Erhan, Aaron Courville, Yoshua Bengio, Pascal Vincent