PSL Summer School on High Dimensional Probability and Algorithms - HDPA 2019

Description

This one week summer school belongs to the PSL-maths program of the PSL university. It is devoted to High dimensional probability and algorithms. The targeted audience is young and less young mathematicians starting from the PhD level. The school took place in ÉNS Paris, from July 1 to 5, 2019.

This school is supported by PSL, CNRS, CEREMADE, ÉNS, and IUF. The organizers are Claire Boyer (Sorbonne Université & ÉNS), Djalil Chafaï (Paris-Dauphine/PSL), and Joseph Lehec (Paris-Dauphine/PSL & ÉNS/PSL). 

The program consists in two 9-hour courses:

Sébastien Bubeck (Microsoft Research) 

Some geometric aspects of randomized online decision making. 

Joel Tropp (California Institute of Technology) 

Random matrix theory and computational linear algebra.

Six 45-minute talks: 

Yohann De Castro (École des Ponts) 

Spectral convergence of random graphs and a focus on random geometric graphs. 

Alexandra Carpentier (U. Potsdam) 

Introduction to some problems of composite and minimax hypothesis testing. 

Olga Klopp (ESSEC / CREST) 

Sparse Network Estimation. 

Laurent Massoulié, (INRIA/Microsoft) 

Planting trees in graphs, and finding them back. 

Nicolas Verzelen (INRA) 

Clustering with the relaxed K-means. 

Lenka Zdeborova (CNRS / CEA Saclay)

Loss landscape and behaviour of algorithms in the spiked matrix-tensor model.

More information on https://hdpa2019.sciencesconf.org/

The video capture was produced technically by Thierry Bohnke and Marianne Herve from OHNK

Cours/Séminaire

Tropp 9/9 - Random matrix theory and computational linear algebra
Cours/Séminaire
00:40:59

Tropp 9/9 - Random matrix theory and computational linear algebra

Tropp
Joel Aaron

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both

Tropp 8/9 - Random matrix theory and computational linear algebra
Cours/Séminaire
01:24:40

Tropp 8/9 - Random matrix theory and computational linear algebra

Tropp
Joel Aaron

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both

Tropp 6/9 - Random matrix theory and computational linear algebra
Cours/Séminaire
00:56:11

Tropp 6/9 - Random matrix theory and computational linear algebra

Tropp
Joel Aaron

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both

Cours/Séminaire

Tropp 7/9 - Random matrix theory and computational linear algebra

Tropp
Joel Aaron

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both

Tropp 5/9 - Random matrix theory and computational linear algebra
Cours/Séminaire
01:01:43

Tropp 5/9 - Random matrix theory and computational linear algebra

Tropp
Joel Aaron

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both

Tropp 4/9 - Random matrix theory and computational linear algebra
Cours/Séminaire
01:00:20

Tropp 4/9 - Random matrix theory and computational linear algebra

Tropp
Joel Aaron

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both

Tropp 3/9 - Random matrix theory and computational linear algebra
Cours/Séminaire
00:57:48

Tropp 3/9 - Random matrix theory and computational linear algebra

Tropp
Joel Aaron

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both

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Conférence

Klopp - Sparse Network Estimation
Conférence
00:39:33

Klopp - Sparse Network Estimation

Klopp
Olga

Inhomogeneous random graph models encompass many network models such as stochastic block models and latent position models. We consider the problem of the statistical estimation of the matrix of

Intervenants