Contextual Bandit for Active Learning: Active Thompson Sampling

Djallel Bouneffouf; Romain Laroche; Tanguy Urvoy; Raphaël Féraud; Robin Allesiardo

doi:10.1007/978-3-319-12637-1_51

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Contextual Bandit for Active Learning: Active Thompson Sampling

Djallel Bouneffouf Romain Laroche Tanguy Urvoy Raphaël Féraud Robin Allesiardo

Year: 2014 Lecture notes in computer science Pages: 405-412 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-319-12637-1_51

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Keywords:

Computer science Oracle Active learning (machine learning) Artificial intelligence Machine learning Sampling (signal processing) Task (project management) Point (geometry) Representation (politics) Initialization Sampling distribution Sample (material) Semi-supervised learning Mathematics

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Topics

Machine Learning and Algorithms

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning and Data Classification

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Bandit Algorithms Research

Social Sciences → Decision Sciences → Management Science and Operations Research

Contextual Bandit for Active Learning: Active Thompson Sampling

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