Lectures on “High-Dimensional Data Analysis” (GDR MIA)

FRUMAM, St Charles, Marseille

Date(s) : 21/10/2021 - 22/10/2021   iCal
0 h 00 min

In the frame of the GdR MIA

The analysis of high-dimensional data sets is now ubiquitous in many applications and blends tools from probability theory, machine learning, geometry, graph theory, statistics and optimization. The objective of this two-day event is to gather researchers from different backgrounds to explore recent advances in this field and to stimulate discussions. Special attention will be given to young researchers who are encouraged to participate with an introductory lecture on the first day and dedicated funding for their travel and accommodation.

Confirmed speakers

Elsa Cazelles (CNRS IRIT, Toulouse)
Lenaïc Chizat (CNRS, Orsay)
Agnès Desolneux (CNRS Centre Borelli, Paris)
Aurélien Garivier (ENS Lyon)
Nicolas Keriven (CNRS GIPSA, Grenoble)
Olga Mula (CEREMADE, Paris)
Geneviève Robin (LaMME, ́Evry)
François-Xavier Vialard (LIGM, Marne-la-Vallée).


Thursday, October, 21th
8:45 Welcome
  • Measure concentration and statistics in high-dimension: an introduction
  • This course is dedicated to students and any person willing to acquire a background in measure concentration.
Friday, November, 15th
9:30-10:00 Coffee and introduction
10.00-10.45 Agnès Desolneux A Wasserstein-type distance in the space of Gaussian mixture models
10.45-11:15 François-Xavier Vialard TBD
11:15-11:45 Break
11:45-12:15 Elsa Cazelles A novel notion of barycenter for probability distributions based on optimal weak mass transport
12:15-14:00 Lunch
14:00-14:45 Lénaïc Chizat Analysis of gradient descent on wide two-layer ReLU neural networks
14:45-15:15 Geneviève Robin Outliers detection in networks with missing links
15:15-15:45 Break
15:45-16:15 Nicolas Keriven Graph neural networks on large random graphs: convergence, stability, universality
16:15-16:45 Olga Mula Model order reduction by interpolation in Wasserstein spaces
16:45-17:00 Conclusions


FRUMAM, St Charles (2ème étage)


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