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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
Position-Based Multiple-Play Bandits with Thompson Sampling—0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control—0
Partially Observable Online Change Detection via Smooth-Sparse Decomposition—0
Bandits Under The Influence (Extended Version)—0
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
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