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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 491–500 of 655 papers

TitleStatusHype
Ordinal Bayesian Optimisation—0
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space—0
Parallel Bayesian Optimization Using Satisficing Thompson Sampling for Time-Sensitive Black-Box Optimization—0
Parallel Contextual Bandits in Wireless Handover Optimization—0
Parallelizing Thompson Sampling—0
Partial Likelihood Thompson Sampling—0
Partially Observable Contextual Bandits with Linear Payoffs—0
Partially Observable Online Change Detection via Smooth-Sparse Decomposition—0
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits—0
Planning and Learning in Risk-Aware Restless Multi-Arm Bandit Problem—0
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