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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 201–210 of 655 papers

TitleStatusHype
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
DISCO: An End-to-End Bandit Framework for Personalised Discount Allocation—0
Discounted Thompson Sampling for Non-Stationary Bandit Problems—0
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning—0
Distributed Thompson Sampling—0
Adaptive Combinatorial Allocation—0
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes—0
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits—0
Double-Linear Thompson Sampling for Context-Attentive Bandits—0
A Copula approach for hyperparameter transfer learning—0
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