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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 111–120 of 655 papers

TitleStatusHype
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
On the Importance of Uncertainty in Decision-Making with Large Language Models—0
Meta Learning in Bandits within Shared Affine Subspaces—0
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food—0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
TS-RSR: A provably efficient approach for batch Bayesian Optimization—0
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems—0
Epsilon-Greedy Thompson Sampling to Bayesian Optimization—0
Influencing Bandits: Arm Selection for Preference Shaping—0
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