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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 171–180 of 655 papers

TitleStatusHype
Adaptive Data Augmentation for Thompson Sampling—0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data—0
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning—0
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems—0
Aligning AI Agents via Information-Directed Sampling—0
Asynchronous Multi Agent Active Search—0
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization—0
Adaptive Combinatorial Allocation—0
A Change-Detection Based Thompson Sampling Framework for Non-Stationary Bandits—0
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