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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 326–350 of 655 papers

TitleStatusHype
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
GuideBoot: Guided Bootstrap for Deep Contextual Bandits—0
No Regrets for Learning the Prior in Bandits—0
Metalearning Linear Bandits by Prior Update—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Markov Decision Process modeled with Bandits for Sequential Decision Making in Linear-flow—0
Random Effect Bandits—0
Thompson Sampling for Unimodal Bandits—0
Thompson Sampling with a Mixture Prior—0
Multi-armed Bandit Algorithms on System-on-Chip: Go Frequentist or Bayesian?—0
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms—0
Parallelizing Thompson Sampling—0
Kolmogorov-Smirnov Test-Based Actively-Adaptive Thompson Sampling for Non-Stationary Bandits—0
Asymptotically Optimal Bandits under Weighted Information—0
Diffusion Approximations for Thompson Sampling—0
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