| A random matrix approach to neural networks C Louart, Z Liao, R Couillet The Annals of Applied Probability 28 (2), 1190-1248, 2018 | 202 | 2018 |
| Random matrix theory proves that deep learning representations of gan-data behave as gaussian mixtures MEA Seddik, C Louart, M Tamaazousti, R Couillet International Conference on Machine Learning, 8573-8582, 2020 | 76 | 2020 |
| Concentration of measure and large random matrices with an application to sample covariance matrices C Louart, R Couillet arXiv preprint arXiv:1805.08295, 2018 | 51 | 2018 |
| The unexpected deterministic and universal behavior of large softmax classifiers MEA Seddik, C Louart, R Couillet, M Tamaazousti International Conference on Artificial Intelligence and Statistics, 1045-1053, 2021 | 11 | 2021 |
| Spectral properties of sample covariance matrices arising from random matrices with independent non identically distributed columns C Louart, R Couillet arXiv preprint arXiv:2109.02644, 2021 | 8 | 2021 |
| A concentration of measure and random matrix approach to large-dimensional robust statistics C Louart, R Couillet The Annals of Applied Probability 32 (6), 4737-4762, 2022 | 7 | 2022 |
| A random matrix and concentration inequalities framework for neural networks analysis C Louart, R Couillet 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 7 | 2018 |
| Harnessing neural networks: A random matrix approach C Louart, R Couillet 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 7 | 2017 |
| Sharp bounds for the concentration of the resolvent in convex concentration settings C Louart arXiv preprint arXiv:2201.00284, 2022 | 5 | 2022 |
| Concentration of measure and generalized product of random vectors with an application to hanson-wright-like inequalities C Louart, R Couillet arXiv preprint arXiv:2102.08020, 2021 | 5 | 2021 |
| Concentration of solutions to random equations with concentration of measure hypotheses C Louart, R Couillet arXiv preprint arXiv:2010.09877, 2020 | 4 | 2020 |
| Large dimensional asymptotics of multi-task learning M Tiomoko, C Louart, R Couillet ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 2 | 2020 |
| Random matrix theory proves that deep learning representations of gan-data behave as gaussian mixtures M El Amine Seddik, C Louart, M Tamaazousti, R Couillet arXiv e-prints, arXiv: 2001.08370, 2020 | 2 | 2020 |
| Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting R Ilbert, M Tiomoko, C Louart, A Odonnat, V Feofanov, T Palpanas, ... arXiv preprint arXiv:2406.10327, 2024 | | 2024 |
| Operation with concentration inequalities and Conjugate of parallel sum C Louart arXiv preprint arXiv:2402.08206, 2024 | | 2024 |
| Enhancing Multivariate Time Series Forecasting via Multi-Task Learning and Random Matrix Theory R Ilbert, M Tiomoko, C Louart, V Feofanov, T Palpanas, I Redko NeurIPS Workshop on Time Series in the Age of Large Models, 2024 | | 2024 |
| Random matrix theory and concentration of the measure theory for the study of high dimension data processing. C Louart Université Grenoble Alpes [2020-....], 2023 | | 2023 |
| Théorie des matrices aléatoires et concentration de la mesure pour l'analyse d’algorithmes de traitement de données en grande dimension. C Louart Université Grenoble Alpes, 2023 | | 2023 |
| Concentration of solutions to random equations with concentration of measure hypotheses R Couillet, C Louart | | 2020 |
| A Concentration of Measure Framework to study convex problems and other implicit formulation problems in machine learning C Louart arXiv preprint arXiv:2010.09877, 2020 | | 2020 |
Romain COUILLETUGA IDEX DataScience Chair, GIPSA-lab, University Grenoble-AlpesVahvistettu sähköpostiosoite verkkotunnuksessa gipsa-lab.grenoble-inp.fr
