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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 101–125 of 655 papers

TitleStatusHype
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data—0
Adaptive Combinatorial Allocation—0
Automatic Ensemble Learning for Online Influence Maximization—0
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning—0
Bag of Policies for Distributional Deep Exploration—0
BanditCAT and AutoIRT: Machine Learning Approaches to Computerized Adaptive Testing and Item Calibration—0
Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control—0
Bandit Convex Optimization: sqrtT Regret in One Dimension—0
Bandit Learning for Diversified Interactive Recommendation—0
Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization—0
Bandit Models of Human Behavior: Reward Processing in Mental Disorders—0
Bandit Policies for Reliable Cellular Network Handovers in Extreme Mobility—0
Bandits Under The Influence (Extended Version)—0
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization—0
Batch Bayesian Optimization for Replicable Experimental Design—0
Adaptive Sensor Placement for Continuous Spaces—0
Batched Thompson Sampling—0
Batched Thompson Sampling for Multi-Armed Bandits—0
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits—0
Bayesian Bandit Algorithms with Approximate Inference in Stochastic Linear Bandits—0
An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling—0
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies—0
A Note on Information-Directed Sampling and Thompson Sampling—0
Bayesian decision-making under misspecified priors with applications to meta-learning—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
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