An Analysis of Ensemble Sampling

Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track

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Chao Qin, Zheng Wen, Xiuyuan Lu, Benjamin Van Roy


Ensemble sampling serves as a practical approximation to Thompson sampling when maintaining an exact posterior distribution over model parameters is computationally intractable. In this paper, we establish a regret bound that ensures desirable behavior when ensemble sampling is applied to the linear bandit problem. This represents the first rigorous regret analysis of ensemble sampling and is made possible by leveraging information-theoretic concepts and novel analytic techniques that may prove useful beyond the scope of this paper.