[16th EPFL CIS - RIKEN AIP Joint Seminar] Talks by Minh Ha Quang, RIKEN AIP

Wed, 13 Jul 2022 17:00 - 18:00 JST
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Registration closes 13 Jul 18:00
-The passcode: X6dS8d05Vb -Time Zone:JST -The seats are available on a first-come-first-served basis. -When the seats are fully booked, we may stop accepting applications. -Simultaneous interpretation will not be available.
There is room for 263 more people


EPFL CIS and RIKEN AIP started a seminar, titled “EPFL CIS - RIKEN AIP Joint Seminar series" from October, 2021.

EPFL is located in Switzerland and is one of the most vibrant and cosmopolitan science and technology institutions. EPFL has both a Swiss and international vocation and focuses on three missions: teaching, research and innovation.

The Center for Intelligent Systems (CIS) at EPFL, a joint initiative of the schools ENAC, IC, SB, STI and SV seeks to advance research and practice in the strategic field of intelligent systems.

RIKEN is Japan's largest comprehensive research institution renowned for high-quality research in a diverse range of scientific disciplines.

RIKEN Center for Advanced Intelligence Project (AIP) houses more than 30 research teams ranging from fundamentals of machine learning and optimization, applications in medicine, materials, and disaster, to analysis of ethics and social impact of artificial intelligence.

【The 16th Seminar】

Date and Time: July 13th 5:00pm – 6:00pm(JST)
Venue:Zoom webinar

Language: English

Speaker: Minh Ha Quang, RIKEN AIP

Title: Regularized information geometric and optimal transport distances between covariance operators and Gaussian processes
Information 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. All of the presented formulations admit closed form expressions that can be efficiently computed and applied practically.

Minh Ha Quang is currently a unit leader at RIKEN AIP where he leads the Functional Analytic Learning Unit. He received the PhD degree in Mathematics from Brown University (RI, USA). Before joining AIP, he was a researcher at the Italian Institute of Technology in Genova, Italy. His current research focuses on functional analytic and geometrical methods in machine learning and statistics.

All participants are required to agree with the AIP Seminar Series Code of Conduct.
Please see the URL below.

RIKEN AIP will expect adherence to this code throughout the event. We expect cooperation from all participants to help ensure a safe environment for everybody.

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