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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 576–600 of 655 papers

TitleStatusHype
Reinforcement learning techniques for Outer Loop Link Adaptation in 4G/5G systems—0
Streaming kernel regression with provably adaptive mean, variance, and regularization—0
Counterfactual Data-Fusion for Online Reinforcement Learners—0
Taming Non-stationary Bandits: A Bayesian Approach—0
Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms: A Case with Bounded Regret—0
Calibrated Fairness in Bandits—0
A Practical Method for Solving Contextual Bandit Problems Using Decision Trees—0
Bandit Models of Human Behavior: Reward Processing in Mental Disorders—0
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space—0
Thompson Sampling for the MNL-Bandit—0
Scalable Generalized Linear Bandits: Online Computation and Hashing—0
Asynchronous Parallel Bayesian Optimisation via Thompson SamplingCode0
A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data—0
AIXIjs: A Software Demo for General Reinforcement LearningCode0
Ensemble Sampling—0
Posterior sampling for reinforcement learning: worst-case regret bounds—0
Adaptive Rate of Convergence of Thompson Sampling for Gaussian Process Optimization—0
Context Attentive Bandits: Contextual Bandit with Restricted Context—0
Multi-dueling Bandits with Dependent Arms—0
Mostly Exploration-Free Algorithms for Contextual BanditsCode0
Time-Sensitive Bandit Learning and Satisficing Thompson Sampling—0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Thompson Sampling for Linear-Quadratic Control Problems—0
Horde of Bandits using Gaussian Markov Random Fields—0
QoS-Aware Multi-Armed Bandits—0
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