The Palaisien Seminar

« Le Séminaire Palaisien » | David Degras & Jordan Serres

Bandeau image
Séminaire Le Palaisien
Date de tri
Lieu de l'événement
Inria Saclay, Amphitheater Sophie Germain


Le "Séminaire Palaisien" gathers, every first Tuesday of the month, the vast research community of Saclay around statistics and machine learning.
Corps de texte

Each seminar session is divided into two scientific presentations of 40 minutes each: 30 minutes of presentation and 10 minutes of questions.

David Degras and Jordan Serres will host the 2023 october session!

Registration is free but mandatory, subject to availability. A buffet will be served at the end of the seminar.

Nom de l'accordéon
12:15pm - 12:55pm : David Degras | "Sparse estimation and model segmentation via the Sparse Group Fused Lasso"
Texte dans l'accordéon

Abstract : This talk introduces the sparse group fused lasso (SGFL), a statistical framework for segmenting sparse regression models of multivariate time series. To compute solutions of the SGFL, a nonsmooth and nonseparable convex program, we develop a hybrid optimization method that is fast, requires no internal tuning, and is guaranteed to converge to a global minimizer. In numerical experiments, the proposed algorithm compares favorably to the state of the art with respect to computation time and numerical accuracy; benefits are particularly substantial in high dimension. Statistical performance is satisfactory in recovering nonzero regression coefficients and excellent in change point detection. Applications to air quality and neuroimaging data will be presented. The hybrid algorithm is implemented in an R package available here.

Nom de l'accordéon
1pm - 1:45pm : Jordan Serres | "Concentration of empirical barycenters in Non Positively Curved metric spaces"
Texte dans l'accordéon

Abstract : Barycenters in non-Euclidean geometries are the most natural extension of linear averaging. They are widely used in shape statistics, optimal transport and matrix analysis. In this talk, I will present their asymptotic properties, i.e. law of large numbers. I will also present some more recent results about their non-asymptotic concentration rate.