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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 561–570 of 655 papers

TitleStatusHype
Aligning AI Agents via Information-Directed Sampling—0
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data—0
An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces—0
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring—0
Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms—0
Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization—0
Analysis of Thompson Sampling for Graphical Bandits Without the Graphs—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits—0
An Analysis of Ensemble Sampling—0
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