2022 - T3 - WS1 - Non-Linear and High Dimensional Inference

Collection 2022 - T3 - WS1 - Non-Linear and High Dimensional Inference

Organisateur(s) Aamari, Eddie ; Aaron, Catherine ; Chazal, Frédéric ; Fischer, Aurélie ; Hoffmann, Marc ; Le Brigant, Alice ; Levrard, Clément ; Michel, Bertrand

Date(s) 03/10/2022 - 07/10/2022

URL associée https://indico.math.cnrs.fr/event/7545/

16 21

Learning a partial correlation graph using only a few covariance queries

De Vasiliki Velona

In settings where the covariance matrix is too large to even store, we would like to learn the partial correlation graph with as few covariance queries as possible (in a partial correlation graph, an edge exists if the corresponding entry in the inverse covariance matrix is non-zero). In recent work with Gabor Lugosi, Jakub Truszkowski, and Piotr Zwiernik, we showed that it is possible to use only a quasi-linear number of queries if the inverse covariance matrix is sparse enough, in the sense that the partial correlation graph resembles a tree on a global scale. I will explain these results and discuss extensions and applications.

Informations sur la vidéo

Date de captation 07/10/2022
Date de publication 03/05/2024
Institut IHP
Langue Anglais
Audience Chercheurs, Doctorants, Etudiants
Réalisateur(s) Alexandre Duplessis, Marco Perez
Format MP4
Lieu Amphitheater Hermite, IHP

Données de citation

DOI 10.57987/IHP.2022.T3.WS1.016
Citer cette vidéo Velona, Vasiliki (07/10/2022). Learning a partial correlation graph using only a few covariance queries. IHP. Audiovisual resource. DOI: 10.57987/IHP.2022.T3.WS1.016
URL https://dx.doi.org/10.57987/IHP.2022.T3.WS1.016

Domaine(s)

Calcul

Codes MSC

62A09 Graphical methods

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