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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 531–540 of 655 papers

TitleStatusHype
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms—0
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
A Copula approach for hyperparameter transfer learning—0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks—0
Active Reinforcement Learning with Monte-Carlo Tree Search—0
Active Search for High Recall: a Non-Stationary Extension of Thompson Sampling—0
AdaptEx: A Self-Service Contextual Bandit Platform—0
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