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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 141–150 of 655 papers

TitleStatusHype
The Sliding Regret in Stochastic Bandits: Discriminating Index and Randomized Policies—0
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study—0
Probabilistic Inference in Reinforcement Learning Done Right—0
A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT—0
Adaptive Interventions with User-Defined Goals for Health Behavior ChangeCode0
Exploration via linearly perturbed loss minimisation—0
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds—0
Batch Bayesian Optimization for Replicable Experimental Design—0
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning—0
Dual-Directed Algorithm Design for Efficient Pure Exploration—0
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