4 papers
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…
Intrinsic Bayesian Cramér-Rao Bound with an Application to Covariance Matrix Estimation
Florent Bouchard, Alexandre Renaux, Guillaume Ginolhac +1
This paper presents a new performance bound for estimation problems where the parameter to estimate lies in a Riemannian manifold (a smooth manifold endowed with a Riemannian metri…
Random matrix theory improved Fréchet mean of symmetric positive definite matrices
Florent Bouchard, Ammar Mian, Malik Tiomoko +2
In this study, we consider the realm of covariance matrices in machine learning, particularly focusing on computing Fréchet means on the manifold of symmetric positive definite ma…
A New Statistic for Testing Covariance Equality in High-Dimensional Gaussian Low-Rank Models
Rémi Beisson, Pascal Vallet, Audrey Giremus +1
In this paper, we consider the problem of testing equality of the covariance matrices of L complex Gaussian multivariate time series of dimension . We study the special case wh…