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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 211–220 of 655 papers

TitleStatusHype
Only Pay for What Is Uncertain: Variance-Adaptive Thompson Sampling—0
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning—0
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms—0
Thompson Sampling for Linear Bandit Problems with Normal-Gamma Priors—0
The Choice of Noninformative Priors for Thompson Sampling in Multiparameter Bandit Models—0
When Combinatorial Thompson Sampling meets Approximation Regret—0
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits—0
A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation—0
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration—0
Leveraging Demonstrations to Improve Online Learning: Quality Matters—0
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