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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 261–270 of 655 papers

TitleStatusHype
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model—0
Contextual Bandit with Herding Effects: Algorithms and Recommendation Applications—0
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization—0
A Federated Online Restless Bandit Framework for Cooperative Resource Allocation—0
Contextual Bandits with Non-Stationary Correlated Rewards for User Association in MmWave Vehicular Networks—0
Contextual Bandits for Advertising Budget Allocation—0
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food—0
Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling—0
Context Attribution with Multi-Armed Bandit Optimization—0
A Reliability-aware Multi-armed Bandit Approach to Learn and Select Users in Demand Response—0
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