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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 401–410 of 655 papers

TitleStatusHype
Causal Bandits without prior knowledge using separating sets—0
Thompson Sampling for Unsupervised Sequential Selection—0
A Change-Detection Based Thompson Sampling Framework for Non-Stationary Bandits—0
Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling—0
Contextual Bandits for Advertising Budget Allocation—0
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses—0
Reinforcement Learning with Trajectory Feedback—0
Lenient Regret for Multi-Armed Bandits—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Greedy Bandits with Sampled Context—0
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