Publications
2026
C. Yaakoubi, C. Louart, M. Tiomoko, Z. Liao, “Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization”, ICML 2026 [paper].
C. Louart, “Resolvent convergence for sample covariance matrices with general covariance profiles and quadratic-form control”, preprint [paper].
C. Louart, “A Central Limit Theorem for Regularized M-Estimators”, preprint [paper].
M. Tiomoko, H. Cherkaoui, M. E. A. Seddik, C. Louart, E. Schnoor, B. Kégl, “High-Dimensional Analysis of Bootstrap Ensemble Classifiers”, AISTATS 2026 [paper].
Y. Moahker, M. Tiomoko, C. Louart, Z. Liao, “A Random Matrix Perspective of Echo State Networks: From Precise Bias-Variance Characterization to Optimal Regularization”, ICASSP 2026 [paper].
2025
C. Louart, “Operation with concentration inequalities”, submitted [paper].
C. Louart, S. Tan, “Universal concentration for sums under arbitrary dependence”, submitted [paper].
2024
R. Ilbert, M. Tiomoko, C. Louart, V. Feofanov, T. Palpanas, I. Redko, “Enhancing Multivariate Time Series Forecasting via Multi-Task Learning and Random Matrix Theory”, NeurIPS Workshop on Time Series in the Age of Large Models [paper].
2023
C. Louart, “Random matrix theory and concentration of measure theory for the study of high-dimensional data processing”, PhD thesis [thesis manuscript].
2022
C Louart, R Couillet, “A Concentration of Measure and Random Matrix Approach to Large Dimensional Robust Statistics”, The Annals of Applied Probability [paper].
2021
MEA.Seddik, C.Louart, R.Couillet, M.Tamaazousti, “The Unexpected Deterministic and Universal Behavior of Large Softmax Classifiers”, AISTATS’2021. [paper] [bibtex][github][interactive julia code (takes some minuts to charge)].
2020
MEA.Seddik, C.Louart, M.Tamaazousti, R.Couillet, “Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures”, ICML’2020. [paper] [arxiv] [slides] [bibtex].
2019
C Louart, R Couillet, “Concentration of Measure and Large Random Matrices with an application to Sample Covariance Matrices”, arXiv preprint arXiv:1805.08295 (2019) [paper] [paper].
M Tiomoko, C Louart, R Couillet, “Large Dimensional Asymptotics of Multi-task Learning”, IEEE International Conference on Acoustics, Speech and Signal Processings (ICASSP’19) [paper].
C. Louart, R. Couillet, “A concentration of measure perspective to robust statistics”, IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP’19)[paper].
2018
C Louart, Z Liao, R Couillet, “A random matrix approach to neural networks”, The Annals of Applied Probability 28 (2), 1190-1248 [paper].
C Louart, R Couillet, “A Random Matrix and Concentration Inequalities Framework for Neural Networks Analysis” IEEE International Conference on Acoustics, Speech and Signal Processings (ICASSP’18) [paper].
2017
C Louart, R Couillet, “Harnessing neural networks: A random matrix approach”, IEEE International Conference on Acoustics, Speech and Signal Processings (ICASSP’17) [paper].