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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 151–175 of 655 papers

TitleStatusHype
Bandits Under The Influence (Extended Version)—0
Analysis of Thompson Sampling for Partially Observable Contextual Multi-Armed Bandits—0
Bandit Policies for Reliable Cellular Network Handovers in Extreme Mobility—0
Bandit Models of Human Behavior: Reward Processing in Mental Disorders—0
Analysis of Thompson Sampling for Graphical Bandits Without the Graphs—0
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits—0
A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Bandit Learning for Diversified Interactive Recommendation—0
Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization—0
Bandit Convex Optimization: sqrtT Regret in One Dimension—0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control—0
Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms—0
Adaptive Experimentation at Scale: A Computational Framework for Flexible Batches—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
Bag of Policies for Distributional Deep Exploration—0
Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring—0
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning—0
Automatic Ensemble Learning for Online Influence Maximization—0
An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces—0
Adaptive Data Augmentation for Thompson Sampling—0
Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling—0
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data—0
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning—0
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems—0
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