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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 1–25 of 655 papers

TitleStatusHype
Robust Policy Switching for Antifragile Reinforcement Learning for UAV Deconfliction in Adversarial Environments—0
Context Attribution with Multi-Armed Bandit Optimization—0
Adaptive Data Augmentation for Thompson Sampling—0
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?—0
Efficient kernelized bandit algorithms via exploration distributions—0
Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget—0
Thompson Sampling in Online RLHF with General Function Approximation—0
Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling—0
Stable Thompson Sampling: Valid Inference via Variance Inflation—0
Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection—0
Representative Action Selection for Large Action-Space Meta-BanditsCode0
Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine—0
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype—0
Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Steering Generative Models with Experimental Data for Protein Fitness OptimizationCode1
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
Dynamic Assortment Selection and Pricing with Censored Preference FeedbackCode0
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