KONE, Cyrille; KAUFMANN, Emilie; RICHERT, Laura

doi:10.48550/arXiv.2307.00424

RICHERT, Laura
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]

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Communication dans un congrès

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NeurIPS 2023, the Thirty-seventh Annual Conference on Neural Information Processing Systems, 2023-12-10, La Nouvelle Orléans. 2023

Résumé en anglais

In this paper we revisit the fixed-confidence identification of the Pareto optimal set in a multi-objective multi-armed bandit model. As the sample complexity to identify the exact Pareto set can be very large, a relaxation allowing to output some additional near-optimal arms has been studied. In this work we also tackle alternative relaxations that allow instead to identify a relevant subset of the Pareto set. Notably, we propose a single sampling strategy, called Adaptive Pareto Exploration, that can be used in conjunction with different stopping rules to take into account different relaxations of the Pareto Set Identification problem. We analyze the sample complexity of these different combinations, quantifying in particular the reduction in sample complexity that occurs when one seeks to identify at most $k$ Pareto optimal arms. We showcase the good practical performance of Adaptive Pareto Exploration on a real-world scenario, in which we adaptively explore several vaccination strategies against Covid-19 in order to find the optimal ones when multiple immunogenicity criteria are taken into account.< Réduire

URI

https://oskar-bordeaux.fr/handle/20.500.12278/194970

DOI

http://dx.doi.org/10.48550/arXiv.2307.00424

Unités de recherche

Bordeaux Population Health Research Center (BPH) - UMR 1219

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Adaptive Algorithms for Relaxed Pareto Set Identification

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