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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 26–50 of 655 papers

TitleStatusHype
Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic LearningCode1
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
Adaptive Grey-Box Fuzz-Testing with Thompson Sampling—0
Adaptive Gating for Single-Photon 3D Imaging—0
A Combinatorial Semi-Bandit Approach to Charging Station Selection for Electric Vehicles—0
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms—0
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits—0
Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches—0
Adaptive Data Augmentation for Thompson Sampling—0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
Adaptive Combinatorial Allocation—0
A Change-Detection Based Thompson Sampling Framework for Non-Stationary Bandits—0
A Batched Multi-Armed Bandit Approach to News Headline Testing—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
An Analysis of Ensemble Sampling—0
Aging Bandits: Regret Analysis and Order-Optimal Learning Algorithm for Wireless Networks with Stochastic Arrivals—0
A General Recipe for the Analysis of Randomized Multi-Armed Bandit Algorithms—0
Accelerating Grasp Exploration by Leveraging Learned Priors—0
A General Theory of the Stochastic Linear Bandit and Its Applications—0
A Formal Solution to the Grain of Truth Problem—0
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization—0
Aligning AI Agents via Information-Directed Sampling—0
AdaptEx: A Self-Service Contextual Bandit Platform—0
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