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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 331–340 of 655 papers

TitleStatusHype
Ensemble Sampling—0
Epinet for Content Cold Start—0
Epsilon-Greedy Thompson Sampling to Bayesian Optimization—0
Estimating prediction error for complex samples—0
Estimating Quality in Multi-Objective Bandits Optimization—0
Etat de l'art sur l'application des bandits multi-bras—0
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning—0
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems—0
Expected Improvement-based Contextual Bandits—0
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