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stat.ML2026
FedSPDnet: Geometry-Aware Federated Deep Learning with SPDnet
Thibault Pautrel, Florent Bouchard, Ammar Mian +1
We introduce two federated learning frameworks for the classical SPDnet model operating on symmetric positive definite (SPD) matrices with Stiefel-constrained parameters. Unlike st…
stat.ML2025
Beyond R-barycenters: an effective averaging method on Stiefel and Grassmann manifolds
Florent Bouchard, Nils Laurent, Salem Said +1
In this paper, the issue of averaging data on a manifold is addressed. While the Fréchet mean resulting from Riemannian geometry appears ideal, it is unfortunately not always avai…
stat.ML2024
Elliptical Wishart distributions: information geometry, maximum likelihood estimator, performance analysis and statistical learning
Imen Ayadi, Florent Bouchard, Frédéric Pascal
This paper deals with Elliptical Wishart distributions - which generalize the Wishart distribution - in the context of signal processing and machine learning. Two algorithms to com…