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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

Showing 541550 of 655 papers

TitleStatusHype
Adaptive Combinatorial Allocation0
Adaptive Data Augmentation for Thompson Sampling0
Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches0
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits0
Adaptive Gating for Single-Photon 3D Imaging0
Adaptive Grey-Box Fuzz-Testing with Thompson Sampling0
Adaptively Learning to Select-Rank in Online Platforms0
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce0
Adaptive Model Selection Framework: An Application to Airline Pricing0
Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D0
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