2011
NIPS
NeurIPS 2011
An Empirical Evaluation of Thompson Sampling
Abstract
Thompson sampling is one of oldest heuristic to address the exploration / exploitation trade-off, but it is surprisingly not very popular in the literature. We present here some empirical results using Thompson sampling on simulated and real data, and show that it is highly competitive. And since this heuristic is very easy to implement, we argue that it should be part of the standard baselines to compare against.
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— thompson sampling
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Hot Topic Early Bird
— multi-armed bandit
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio
Authors
Topics
Artificial Intelligence > Core AI > Multi-Agent Systems
Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Optimization & Theory > Bayesian Inference
Mathematics & Optimization > Optimization > Stochastic Methods
Machine Learning > Optimization & Theory > Stochastic Methods
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Learning Types > Multi-Armed Bandits