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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.

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Showing 41–50 of 655 papers

TitleStatusHype
Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Dynamic Decision-Making under Model Misspecification—0
Addressing Missing Data Issue for Diffusion-based RecommendationCode0
Thompson Sampling-like Algorithms for Stochastic Rising Bandits—0
Leveraging Offline Data from Similar Systems for Online Linear Quadratic Control—0
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
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