Abstract
This paper is devoted to the study of the max K-armed bandit problem, which consists in sequentially allocating resources in order to detect extreme values. Our contribution is twofold. We first significantly refine the analysis of the ExtremeHunter algorithm carried out in Carpentier and Valko (2014), and next propose an alternative approach, showing that, remarkably, Extreme Bandits can be reduced to a classical version of the bandit problem to a certain extent. Beyond the formal analysis, these two approaches are compared through numerical experiments.
Cite
CITATION STYLE
Achab, M., Clémençon, S., Garivier, A., Sabourin, A., & Vernade, C. (2017). Max K-Armed Bandit: On the ExtremeHunter Algorithm and Beyond. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10535 LNAI, pp. 389–404). Springer Verlag. https://doi.org/10.1007/978-3-319-71246-8_24
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
