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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 241–250 of 655 papers

TitleStatusHype
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Ensemble Sampling—0
Epinet for Content Cold Start—0
Epsilon-Greedy Thompson Sampling to Bayesian Optimization—0
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies—0
Estimating prediction error for complex samples—0
A Copula approach for hyperparameter transfer learning—0
Etat de l'art sur l'application des bandits multi-bras—0
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning—0
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation—0
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