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Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

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Showing 491–500 of 655 papers

TitleStatusHype
Thompson Sampling Itself is Differentially Private—0
Thompson Sampling-like Algorithms for Stochastic Rising Bandits—0
Thompson Sampling on Asymmetric α-Stable Bandits—0
Thompson Sampling on Symmetric α-Stable Bandits—0
Thompson Sampling Regret Bounds for Contextual Bandits with sub-Gaussian rewards—0
Thompson Sampling under Bernoulli Rewards with Local Differential Privacy—0
Thompson Sampling with a Mixture Prior—0
Thompson Sampling with Diffusion Generative Prior—0
Thompson sampling with the online bootstrap—0
Thompson Sampling with Unrestricted Delays—0
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