Short description: Variational inference approximates a target distribution with a simpler one. While traditional inference minimizes the “inclusive” KL-divergence, several algorithms have recently been proposed to minimize other divergences. Experimentally, however, these algorithms often seem to fail to converge. In this paper we analyze the variance of the underlying estimators for these papers. Our results … Continue reading "Paper: On the Difficulty of Unbiased Alpha Divergence Minimization"

We present several classes of reinforcement learning algorithms that safely generalize to Markov decision processes (MDPs) not seen during training. Specifically, we study the setting in which some set of MDPs is accessible for training. For various definitions of safety, our algorithms give probabilistic guarantees that agents can safely generalize to MDPs that are sampled … Continue reading "Paper: High Confidence Generalization for Reinforcement Learning"