Harnessing neural networks: A random matrix approach. - Cosme Louart; Romain Couillet (ICASSP, 2017)

A Random Matrix Approach to Neural Networks. - Cosme Louart; Zhenyu Liao 001; Romain Couillet (CoRR, 2017)

A Random Matrix and Concentration Inequalities Framework for Neural Networks Analysis. - Cosme Louart; Romain Couillet (ICASSP, 2018)

A Concentration of Measure Perspective to Robust Statistics. - Cosme Louart; Romain Couillet (CAMSAP, 2019)

Large Dimensional Asymptotics of Multi-Task Learning. - Malik Tiomoko; Cosme Louart; Romain Couillet (ICASSP, 2020)

Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures. - Mohamed El Amine Seddik; Cosme Louart; Mohamed Tamaazousti; Romain Couillet (ICML, 2020)

The Unexpected Deterministic and Universal Behavior of Large Softmax Classifiers. - Mohamed El Amine Seddik; Cosme Louart; Romain Couillet; Mohamed Tamaazousti (AISTATS, 2021)

Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting. - Romain Ilbert; Malik Tiomoko; Cosme Louart; Ambroise Odonnat; Vasilii Feofanov; Themis Palpanas; Ievgen Redko (NeurIPS, 2024)

High-Dimensional Analysis of Bootstrap Ensemble Classifiers. - Hamza Cherkaoui; Malik Tiomoko; Mohamed El Amine Seddik; Cosme Louart; Ekkehard Schnoor; Balázs Kégl (CoRR, 2025)