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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
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers—0
Robust Policy Switching for Antifragile Reinforcement Learning for UAV Deconfliction in Adversarial Environments—0
Robust Thompson Sampling Algorithms Against Reward Poisoning Attacks—0
Safe Linear Leveling Bandits—0
Safe Linear Thompson Sampling with Side Information—0
Sample-based Dynamic Hierarchical Transformer with Layer and Head Flexibility via Contextual Bandit—0
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity—0
Sampling Acquisition Functions for Batch Bayesian Optimization—0
Satisficing in Time-Sensitive Bandit Learning—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
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