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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

Showing 8190 of 655 papers

TitleStatusHype
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte CarloCode0
Efficient Exploration through Bayesian Deep Q-NetworksCode0
Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo RecommendationsCode0
Evolutionary Multi-Armed Bandits with Genetic Thompson SamplingCode0
Fast, Precise Thompson Sampling for Bayesian OptimizationCode0
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender SystemsCode0
Bandit Learning with Implicit FeedbackCode0
Improving Portfolio Optimization Results with Bandit NetworksCode0
Information-Directed Exploration for Deep Reinforcement LearningCode0
Automated Creative Optimization for E-Commerce AdvertisingCode0
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