September 5, 2022 to December 9, 2022
Europe/Paris timezone
Financial support for the participation to the quarter is now closed

Vasiliki Velona - Learning a partial correlation graph using only a few covariance queries.

Oct 7, 2022, 11:30 AM
Amphitheater Hermite, IHP

Amphitheater Hermite, IHP


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.

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