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DTSTART;TZID=Europe/Paris:20241015T133000
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DTSTAMP:20260422T080244
CREATED:20241001T142628Z
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UID:8568-1728999000-1729004400@www.etis-lab.fr
SUMMARY:Séminaire DATAAI - Minh Ha Quang
DESCRIPTION:An optimal transport and information geometric framework for Gaussian processes\nAbstract:\nInformation geometry (IG) and Optimal transport (OT) have been attracting much research attention in various fields\, in particular machine learning and statistics. In this talk\, we present results on the generalization of IG and OT distances for finite-dimensional Gaussian measures to the setting of infinite-dimensional Gaussian measures and Gaussian processes. Our focus is on the Entropic Regularization of the 2-Wasserstein distance and the generalization of the Fisher-Rao distance and related quantities. In both settings\, regularization leads to many desirable theoretical properties\, including in particular dimension-independent convergence and sample complexity. The mathematical formulation involves the interplay of IG and OT with Gaussian processes and the methodology of reproducing kernel Hilbert spaces (RKHS). All of the presented formulations admit closed form expressions that can be efficiently computed and applied practically. The mathematical formulations will be illustrated with numerical experiments on Gaussian processes. \nBio:\nMinh Ha Quang is the team leader of the Functional Analytic Learning team in the RIKEN Center for Advanced Intelligence Project (AIP)\, Tokyo\, JAPAN. He received his PhD in mathematics from Brown University (Providence\, RI\, USA) under the supervision of Stephen Smale. Before joining RIKEN\, he was a researcher at the Pattern Analysis and Computer Vision group at the Italian Institute of Technology (Istituto Italiano di Tecnologia) in Genoa (Genova)\, Italy. Prior to Italy\, he was a postdoctoral researcher at the University of Vienna\, Austria\, and the Humboldt University of Berlin\, Germany. His current research interests focus on machine learning and statistical methodologies using theories and techniques from Functional Analysis and related mathematical fields. In particular\, he has been working on theories and methods involving reproducing kernel Hilbert spaces (RKHS)\, Riemannian geometry\, Matrix and Operator Theory\, Information Geometry\, and Optimal Transport\, especially in the Infinite-Dimensional setting. \nTeams link: https://teams.microsoft.com/l/meetup-join/19%3ameeting_YjA3NjcxODItZTdjNy00Yzg0LThkYjQtNTg2MDhlYmEwMjY0%40thread.v2/0?context=%7b%22Tid%22%3a%22aa8bdaa4-8feb-46c0-b5e6-31c96337579b%22%2c%22Oid%22%3a%22c4e82db7-e9d5-4310-b5c4-91f9189f0cba%22%7d \n 
URL:https://www.etis-lab.fr/event/seminaire-dataai-minh-ha-quang/
LOCATION:ENSEA\, salle 331\, 6 avenue du Ponceau\, Cergy\, 95000\, France
CATEGORIES:ETIS,Seminar
ORGANIZER;CN="Xuan-Son Nguyen":MAILTO:xuan-son.nguyen@ensea.fr
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