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Conference Papers Year : 2021

Indexed Minimum Empirical Divergence for Unimodal Bandits


We consider a multi-armed bandit problem specified by a set of one-dimensional family exponential distributions endowed with a unimodal structure. We introduce IMED-UB, a algorithm that optimally exploits the unimodal-structure, by adapting to this setting the Indexed Minimum Empirical Divergence (IMED) algorithm introduced by Honda and Takemura [2015]. Owing to our proof technique, we are able to provide a concise finite-time analysis of IMED-UB algorithm. Numerical experiments show that IMED-UB competes with the state-of-the-art algorithms.
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Dates and versions

hal-03446617 , version 1 (02-12-2021)



Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard. Indexed Minimum Empirical Divergence for Unimodal Bandits. NeurIPS 2021 - International Conference on Neural Information Processing Systems, Dec 2021, Virtual-only Conference, United States. ⟨hal-03446617⟩
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