Papers
Greedy Model Averaging
Dong Dai, Tong Zhang
High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity
Po-ling Loh, Martin J. Wainwright
Speedy Q-Learning
Mohammad Ghavamzadeh, Hilbert J. Kappen, Mohammad G. Azar et al.
On the Universality of Online Mirror Descent
Nati Srebro, Karthik Sridharan, Ambuj Tewari
Gaussian Process Training with Input Noise
Andrew Mchutchon, Carl E. Rasmussen
Structural equations and divisive normalization for energy-dependent component analysis
Jun-ichiro Hirayama, Aapo Hyvärinen
Confidence Sets for Network Structure
David S. Choi, Patrick J. Wolfe, Edoardo M. Airoldi
Efficient inference in matrix-variate Gaussian models with \iid observation noise
Oliver Stegle, Christoph Lippert, Joris M. Mooij et al.
Learning person-object interactions for action recognition in still images
Vincent Delaitre, Josef Sivic, Ivan Laptev
On Learning Discrete Graphical Models using Greedy Methods
Ali Jalali, Christopher C. Johnson, Pradeep K. Ravikumar
Clustered Multi-Task Learning Via Alternating Structure Optimization
Jiayu Zhou, Jianhui Chen, Jieping Ye
Select and Sample - A Model of Efficient Neural Inference and Learning
Jacquelyn A. Shelton, Abdul S. Sheikh, Pietro Berkes et al.
Robust Lasso with missing and grossly corrupted observations
Nasser M. Nasrabadi, Trac D. Tran, Nam Nguyen
On U-processes and clustering performance
Stéphan J. Clémençcon
Infinite Latent SVM for Classification and Multi-task Learning
Jun Zhu, Ning Chen, Eric P. Xing
A Global Structural EM Algorithm for a Model of Cancer Progression
Ali Tofigh, Erik Sj̦lund, Mattias H̦glund et al.
The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning
Marius Kloft, Gilles Blanchard
Learning large-margin halfspaces with more malicious noise
Phil Long, Rocco Servedio
Higher-Order Correlation Clustering for Image Segmentation
Sungwoong Kim, Sebastian Nowozin, Pushmeet Kohli et al.
Semantic Labeling of 3D Point Clouds for Indoor Scenes
Hema S. Koppula, Abhishek Anand, Thorsten Joachims et al.
A Reinforcement Learning Theory for Homeostatic Regulation
Mehdi Keramati, Boris S. Gutkin
Selecting the State-Representation in Reinforcement Learning
Odalric-ambrym Maillard, Daniil Ryabko, Rémi Munos
Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices
Xianxing Zhang, Lawrence Carin, David B. Dunson