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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 251–260 of 655 papers

TitleStatusHype
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems—0
Convergence Rates of Posterior Distributions in Markov Decision Process—0
Expected Improvement-based Contextual Bandits—0
A study of Thompson Sampling with Parameter h—0
A Formal Solution to the Grain of Truth Problem—0
AdaptEx: A Self-Service Contextual Bandit Platform—0
Contextual Thompson Sampling via Generation of Missing Data—0
Contextual Multi-Armed Bandits for Causal Marketing—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
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