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X-WR-CALNAME:GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
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X-WR-CALDESC:évènements pour GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
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DTSTART:20230326T010000
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DTSTART:20231029T010000
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DTSTART;TZID=Europe/Paris:20230330T140000
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DTSTAMP:20260423T040019
CREATED:20230307T090119Z
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UID:11128-1680184800-1680192000@www.greyc.fr
SUMMARY:Séminaire IMAGE : Louis Filstroff (ENSAI)\, « Multi-Fidelity Bayesian Optimization with Unreliable Information Sources »
DESCRIPTION:Nous aurons le plaisir d’accueillir Louis Filstroff\,  ATER à l’ENSAI (Ecole Nationale de la Statistique et de l’Analyse de l’Information de Rennes)\, qui donnera un séminaire IMAGE\, le jeudi 30 mars 2023 à 14h00 en salle F-200.\nTitre:\nMulti-Fidelity Bayesian Optimization with Unreliable Information Sources \nRésumé:\n\nBayesian optimization (BO) is a powerful framework for optimizing black-box\, expensive-to-evaluate functions. Over the past decade\, many algorithms have been proposed to integrate cheaper\, lower-fidelity approximations of the objective function into the optimization process\, with the goal of converging towards the global optimum at a reduced cost. This task is generally referred to as multi-fidelity Bayesian optimization (MFBO). However\, MFBO algorithms can lead to higher optimization costs than their vanilla BO counterparts\, especially when the low-fidelity sources are poor approximations of the objective function\, therefore defeating their purpose. To address this issue\, we propose rMFBO (robust MFBO)\, a methodology to make any GP-based MFBO scheme robust to the addition of unreliable information sources. rMFBO comes with a theoretical guarantee that its performance can be bound to its vanilla BO analog\, with high controllable probability. We demonstrate the effectiveness of the proposed methodology on a number of numerical benchmarks\, outperforming earlier MFBO methods on unreliable sources. We expect rMFBO to be particularly useful to reliably include human experts with varying knowledge within BO processes.
URL:https://www.greyc.fr/event/seminaire-image-louis-filstroff-ensai-multi-fidelity-bayesian-optimization-with-unreliable-information-sources/
LOCATION:ENSICAEN – Batiment F – Salle F-200\, 6 Bd Maréchal Juin\, Caen\, 14050\, France
CATEGORIES:Image,Seminaire Image
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