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dc.rights.licenseopenen_US
dc.contributor.authorKONE, Cyrille
dc.contributor.authorKAUFMANN, Emilie
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorRICHERT, Laura
dc.date.accessioned2024-04-08T09:02:12Z
dc.date.available2024-04-08T09:02:12Z
dc.date.issued2023
dc.date.conference2023-12-10
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/194970
dc.description.abstractEnIn 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.
dc.language.isoENen_US
dc.title.enAdaptive Algorithms for Relaxed Pareto Set Identification
dc.typeCommunication dans un congrèsen_US
dc.identifier.doi10.48550/arXiv.2307.00424en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.conference.titleNeurIPS 2023, the Thirty-seventh Annual Conference on Neural Information Processing Systemsen_US
bordeaux.countryusen_US
bordeaux.teamSISTM_BPHen_US
bordeaux.conference.cityLa Nouvelle Orléansen_US
hal.invitedouien_US
hal.proceedingsnonen_US
hal.conference.end2023-12-16
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2023&rft.au=KONE,%20Cyrille&KAUFMANN,%20Emilie&RICHERT,%20Laura&rft.genre=unknown


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