598 citations · 1.6k across the 66 of their papers we have counts for
74 papers
Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture
Minh-Toan Nguyen, Romain Couillet
The article considers semi-supervised multitask learning on a Gaussian mixture model (GMM). Using methods from statistical physics, we compute the asymptotic Bayes risk of each tas…
When Random Tensors meet Random Matrices
Mohamed El Amine Seddik, Maxime Guillaud, Romain Couillet
Relying on random matrix theory (RMT), this paper studies asymmetric order- spiked tensor models with Gaussian noise. Using the variational definition of the singular vectors an…
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 matrices in service of ML footprint: ternary random features with no performance loss
Hafiz Tiomoko Ali, Zhenyu Liao, Romain Couillet
In this article, we investigate the spectral behavior of random features kernel matrices of the type ${\bf K} = \mathbb{E}_{\bf w} \left[σ\left({\bf w}^{\sf T}{\bf x}_i\right)σ\lef…
A Random Matrix Perspective on Random Tensors
José Henrique de Morais Goulart, Romain Couillet, Pierre Comon
Tensor models play an increasingly prominent role in many fields, notably in machine learning. In several applications, such as community detection, topic modeling and Gaussian mix…