Max K-armed bandit: On the ExtremeHunter algorithm and beyond
Year: 2017
Type: article
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.
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Award: LabEx Mathématique Hadamard
OpenAlex ID: W2953365597
