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

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

Organizer(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

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

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Learning a partial correlation graph using only a few covariance queries

By 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.

Information about the video

Date of recording 07/10/2022
Date of publication 24/04/2024
Institution IHP
Language English
Audience Researchers, Graduate Students, Students
Director(s) Alexandre Duplessis, Marco Perez
Format MP4
Venue Amphitheater Hermite, IHP

Citation data

DOI 10.57987/IHP.2022.T3.WS1.016
Cite this video 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

Domain(s)

Computation

MSC codes

62A09 Graphical methods

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