451 citations · 1.7k across the 73 of their papers we have counts for
18 papers · 1 filter
Evaluating High-Order Predictive Distributions in Deep Learning
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +3
Most work on supervised learning research has focused on marginal predictions. In decision problems, joint predictive distributions are essential for good performance. Previous wor…
Provably Efficient Reinforcement Learning with Aggregated States
Shi Dong, Benjamin Van Roy, Zhengyuan Zhou
We establish that an optimistic variant of Q-learning applied to a fixed-horizon episodic Markov decision process with an aggregated state representation incurs regret $\tilde{\mat…
Information-Theoretic Confidence Bounds for Reinforcement Learning
Xiuyuan Lu, Benjamin Van Roy
We integrate information-theoretic concepts into the design and analysis of optimistic algorithms and Thompson sampling. By making a connection between information-theoretic quanti…
On the Performance of Thompson Sampling on Logistic Bandits
Shi Dong, Tengyu Ma, Benjamin Van Roy
We study the logistic bandit, in which rewards are binary with success probability and actions and coefficients are within the …
An Information-Theoretic Analysis for Thompson Sampling with Many Actions
Shi Dong, Benjamin Van Roy
Information-theoretic Bayesian regret bounds of Russo and Van Roy capture the dependence of regret on prior uncertainty. However, this dependence is through entropy, which can beco…
On Optimistic versus Randomized Exploration in Reinforcement Learning
Ian Osband, Benjamin Van Roy
We discuss the relative merits of optimistic and randomized approaches to exploration in reinforcement learning. Optimistic approaches presented in the literature apply an optimist…