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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 61–70 of 655 papers

TitleStatusHype
An Unbiased Data Collection and Content Exploitation/Exploration Strategy for Personalization—0
An Empirical Evaluation of Thompson Sampling—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT—0
Active Reinforcement Learning with Monte-Carlo Tree Search—0
A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation—0
A Note on Information-Directed Sampling and Thompson Sampling—0
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—0
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