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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 421–430 of 655 papers

TitleStatusHype
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models—0
Meta Dynamic Pricing: Transfer Learning Across Experiments—0
Meta Learning in Bandits within Shared Affine Subspaces—0
Metalearning Linear Bandits by Prior Update—0
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks—0
Meta-Reinforcement Learning With Informed Policy Regularization—0
Meta-Thompson Sampling—0
Minimal Exploration in Structured Stochastic Bandits—0
TS-RSR: A provably efficient approach for batch Bayesian Optimization—0
Mixed-Variable Bayesian Optimization—0
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