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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 141150 of 655 papers

TitleStatusHype
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits0
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits0
Adaptive Gating for Single-Photon 3D Imaging0
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles0
Batched Thompson Sampling for Multi-Armed Bandits0
Batched Thompson Sampling0
An Analysis of Ensemble Sampling0
Batch Bayesian Optimization for Replicable Experimental Design0
Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits0
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization0
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