Following the Perturbed Leader to Gamble at Multi-armed Bandits

DOI10.1007/978-3-540-75225-7_16zbMath1142.68398OpenAlexW1568674531MaRDI QIDQ3520057

Tapio Elomaa, Jussi Kujala

Publication date: 19 August 2008

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-75225-7_16

Mathematics Subject Classification ID

Computational learning theory (68Q32) Stopping times; optimal stopping problems; gambling theory (60G40)

Uses Software

• AdaBoost.MH

Cites Work

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• The weighted majority algorithm
• A decision-theoretic generalization of on-line learning and an application to boosting
• Adaptive routing with end-to-end feedback
• Robbing the bandit
• Learning Theory
• The Nonstochastic Multiarmed Bandit Problem
• Learning Theory and Kernel Machines
• Algorithmic Learning Theory
• Prediction, Learning, and Games
• Learning Theory
• Some aspects of the sequential design of experiments

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