OPT 2011: 4th International Workshop on Optimization for Machine Learning
Accepted Papers
- A. Agarwal and J. Duchi. Stochastic optimization with non-i.i.d. noise
- J. Bierkens and B. Kappen. Online solution of the average cost Kullback-Leibler optimization problem
- D. Chen, D. Batra, Bill. Freeman, and M. Kimo. Group Norm for Learning Latent Structural SVMs
- M. Dudik, Z. Harchaoui, and J. Malick. Learning with matrix gauge regularizers
- R. Fonteneau, D. Ernst, B. Boigelot, and Q. Louveaux. Relaxation Schemes for Min Max Generalization in Deterministic Batch Mode Reinforcement Learning
- S. A. Hong and G. Gordon An Accelerated Gradient Method for Distributed Multi-Agent Planning with Factored MDPs
- M. Kowalski, P. Weiss, A. Gramfort, and S. Anthoine. Accelerating ISTA with an active set strategy
- G. Loosli and S. Canu. Non positive SVM
- J. Mairal and B. Yu Path Coding Penalties for Directed Acyclic Graphs
- K. Scheinberg and D. Goldfarb Fast First-Order Methods for Composite Convex Optimization with Large Steps
- M. Telgarsky Steepest Descent Analysis for Unregularized Linear Prediction with Strictly Convex Penalties
- O. Shamir Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization
- O. Vinyals and D. Povey. Krylov Subspace Descent for Deep Learning
