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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 511–520 of 655 papers

TitleStatusHype
Two-Stage Resource Allocation in Reconfigurable Intelligent Surface Assisted Hybrid Networks via Multi-Player Bandits—0
Uncertainty-Aware Search and Value Models: Mitigating Search Scaling Flaws in LLMs—0
Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making—0
Reinforcement Learning in Credit Scoring and Underwriting—0
Unimodal Thompson Sampling for Graph-Structured Arms—0
Using Adaptive Experiments to Rapidly Help Students—0
Variable Selection via Thompson Sampling—0
Variational Bayesian Optimistic Sampling—0
WAPTS: A Weighted Allocation Probability Adjusted Thompson Sampling Algorithm for High-Dimensional and Sparse Experiment Settings—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
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