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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 21–30 of 655 papers

TitleStatusHype
Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret—0
Bayesian learning of the optimal action-value function in a Markov decision process—0
Neural Contextual Bandits Under Delayed Feedback Constraints—0
Counterfactual Inference under Thompson Sampling—0
Dynamic Assortment Selection and Pricing with Censored Preference FeedbackCode0
Sparse Nonparametric Contextual Bandits—0
Bandit-Based Prompt Design Strategy Selection Improves Prompt OptimizersCode0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces—0
Uncertainty-Aware Search and Value Models: Mitigating Search Scaling Flaws in LLMs—0
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