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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 291–300 of 655 papers

TitleStatusHype
Adaptive Experimentation in the Presence of Exogenous Nonstationary Variation—0
Combinatorial Multi-armed Bandits: Arm Selection via Group Testing—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
Approximate Thompson Sampling for Learning Linear Quadratic Regulators with O(T) Regret—0
Code Repair with LLMs gives an Exploration-Exploitation Tradeoff—0
Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space Exploration Tool for FPGA High-Level Synthesis—0
Approximate information for efficient exploration-exploitation strategies—0
Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks—0
Challenges in Statistical Analysis of Data Collected by a Bandit Algorithm: An Empirical Exploration in Applications to Adaptively Randomized Experiments—0
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems—0
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