Planning in partially observable environments remains a challenging problem, despite significant recent advances in offline approximation techniques. A few online methods have a...
We present four new reinforcement learning algorithms based on actor-critic and natural-gradient ideas, and provide their convergence proofs. Actor-critic reinforcement learning m...
Shalabh Bhatnagar, Richard S. Sutton, Mohammad Gha...
This paper investigates the application of randomized algorithms for large scale SVM learning. The key contribution of the paper is to show that, by using ideas random projections...
We present a new local approximation algorithm for computing MAP and logpartition function for arbitrary exponential family distribution represented by a finite-valued pair-wise ...
Many existing approaches to collaborative filtering can neither handle very large datasets nor easily deal with users who have very few ratings. In this paper we present the Prob...