1 citations · 1 across the 3 of their papers we have counts for
5 papers
PCA-based Multi Task Learning: a Random Matrix Approach
Malik Tiomoko, Romain Couillet, Frédéric Pascal
The article proposes and theoretically analyses a \emph{computationally efficient} multi-task learning (MTL) extension of popular principal component analysis (PCA)-based supervise…
Multi-task learning on the edge: cost-efficiency and theoretical optimality
Sami Fakhry, Romain Couillet, Malik Tiomoko
This article proposes a distributed multi-task learning (MTL) algorithm based on supervised principal component analysis (SPCA) which is: (i) theoretically optimal for Gaussian mix…
Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions
Malik Tiomoko, Romain Couillet
This article proposes a method to consistently estimate functionals of the eigenvalues of the product of two covariance matrices $C_1,C_2\in\mat…
Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Malik Tiomoko, Florent Bouchard, Guillaume Ginholac +1
Relying on recent advances in statistical estimation of covariance distances based on random matrix theory, this article proposes an improved covariance and precision matrix estima…
Random matrix-improved estimation of covariance matrix distances
Romain Couillet, Malik Tiomoko, Steeve Zozor +1
Given two sets and (or ) of random vectors with zero mean and positive definite covar…