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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 301–325 of 655 papers

TitleStatusHype
Variational Bayesian Optimistic Sampling—0
Differentially Private Federated Bayesian Optimization with Distributed Exploration—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes—0
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized RecommendationsCode0
EE-Net: Exploitation-Exploration Neural Networks in Contextual BanditsCode1
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Batched Thompson Sampling—0
Asymptotic Performance of Thompson Sampling in the Batched Multi-Armed Bandits—0
Regularized-OFU: an efficient algorithm for general contextual bandit with optimization oracles—0
Expected Improvement-based Contextual Bandits—0
Apple Tasting Revisited: Bayesian Approaches to Partially Monitored Online Binary Classification—0
Deep Exploration for Recommendation Systems—0
Vaccine allocation policy optimization and budget sharing mechanism using Thompson samplingCode0
Online Learning of Network Bottlenecks via Minimax Paths—0
Machine Learning for Online Algorithm Selection under Censored FeedbackCode0
Thompson Sampling for Bandits with Clustered Arms—0
A Unifying Theory of Thompson Sampling for Continuous Risk-Averse BanditsCode0
A relaxed technical assumption for posterior sampling-based reinforcement learning for control of unknown linear systems—0
Scalable regret for learning to control network-coupled subsystems with unknown dynamics—0
Batched Thompson Sampling for Multi-Armed Bandits—0
Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models—0
Debiasing Samples from Online Learning Using Bootstrap—0
Adaptively Optimize Content Recommendation Using Multi Armed Bandit Algorithms in E-commerce—0
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
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