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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 601–610 of 655 papers

TitleStatusHype
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
Asymptotic Convergence of Thompson Sampling—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Asynchronous Multi Agent Active Search—0
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems—0
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning—0
Automatic Ensemble Learning for Online Influence Maximization—0
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning—0
Bag of Policies for Distributional Deep Exploration—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
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