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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 21–30 of 655 papers

TitleStatusHype
Optimal Thompson Sampling strategies for support-aware CVaR banditsCode1
Federated Bayesian Optimization via Thompson SamplingCode1
Neural Thompson SamplingCode1
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural ProcessesCode1
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start UsersCode1
On Isometry Robustness of Deep 3D Point Cloud Models under Adversarial AttacksCode1
A Tutorial on Thompson SamplingCode1
Robust Policy Switching for Antifragile Reinforcement Learning for UAV Deconfliction in Adversarial Environments—0
Context Attribution with Multi-Armed Bandit Optimization—0
Adaptive Data Augmentation for Thompson Sampling—0
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