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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

Showing 4150 of 655 papers

TitleStatusHype
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits0
An Analysis of Ensemble Sampling0
Aging Bandits: Regret Analysis and Order-Optimal Learning Algorithm for Wireless Networks with Stochastic Arrivals0
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms0
Accelerating Grasp Exploration by Leveraging Learned Priors0
A General Theory of the Stochastic Linear Bandit and Its Applications0
A Formal Solution to the Grain of Truth Problem0
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization0
Aligning AI Agents via Information-Directed Sampling0
AdaptEx: A Self-Service Contextual Bandit Platform0
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