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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 201210 of 655 papers

TitleStatusHype
Online Continuous Hyperparameter Optimization for Generalized Linear Contextual Bandits0
Approximate Thompson Sampling via Epistemic Neural NetworksCode1
A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation0
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration0
Leveraging Demonstrations to Improve Online Learning: Quality Matters0
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits0
Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences ConstraintsCode0
Differentially Private Online Bayesian Estimation With Adaptive TruncationCode0
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles0
Thompson Sampling with Diffusion Generative Prior0
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