| Random matrix improved covariance estimation for a large class of metrics M Tiomoko, R Couillet, F Bouchard, G Ginolhac International Conference on Machine Learning, 6254-6263, 2019 | 13 | 2019 |
| Random matrix-improved estimation of covariance matrix distances R Couillet, M Tiomoko, S Zozor, E Moisan Journal of Multivariate Analysis 174, 104531, 2019 | 10 | 2019 |
| Deciphering and optimizing multi-task learning: a random matrix approach M Tiomoko, H Tiomoko, R Couillet ICLR 2021-9th International Conference on Learning Representations, 2021 | 9 | 2021 |
| Large dimensional analysis and improvement of multi task learning M Tiomoko, R Couillet, H Tiomoko arXiv preprint arXiv:2009.01591, 2020 | 7 | 2020 |
| PCA-based multi-task learning: a random matrix approach M Tiomoko, R Couillet, F Pascal International Conference on Machine Learning, 34280-34300, 2023 | 6 | 2023 |
| Random matrix-improved estimation of the wasserstein distance between two centered gaussian distributions M Tiomoko, R Couillet 2019 27th European Signal Processing Conference (EUSIPCO), 1-5, 2019 | 5 | 2019 |
| Improved estimation of the distance between covariance matrices M Tiomoko, R Couillet, E Moisan, S Zozor ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 5 | 2019 |
| Deciphering lasso-based classification through a large dimensional analysis of the iterative soft-thresholding algorithm M Tiomoko, E Schnoor, MEA Seddik, I Colin, A Virmaux International Conference on Machine Learning, 21449-21477, 2022 | 4 | 2022 |
| Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption V Feofanov, M Tiomoko, A Virmaux International Conference on Machine Learning 202, 10008-10033, 2023 | 2 | 2023 |
| 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 |
| Estimation of covariance matrix distances in the high dimension low sample size regime M Tiomoko, R Couillet 2019 IEEE 8th International Workshop on Computational Advances in Multi …, 2019 | 2 | 2019 |
| Learning from low rank tensor data: A random tensor theory perspective MEA Seddik, M Tiomoko, A Decurninge, M Panov, M Gauillaud Uncertainty in Artificial Intelligence, 1858-1867, 2023 | 1 | 2023 |
| Multi-task learning on the edge: cost-efficiency and theoretical optimality S Fakhry, R Couillet, M Tiomoko arXiv preprint arXiv:2110.04639, 2021 | 1 | 2021 |
| Apprentissage multitâche en grande dimension: classification basée sur les covariances C Doz, M Tiomoko, C Ren, JP Ovarlez GRETSI 2023, 2023 | | 2023 |
| Optimizing Spca-based Continual Learning: A Theoretical Approach C Yang, M Tiomoko, Z Wang The Eleventh International Conference on Learning Representations, 2022 | | 2022 |
| Classification multi-tâches semi-supervisée en grande dimension V Leger, M Tiomoko, R Couillet GRETSI 2022-XXVIIIème Colloque Francophone de Traitement du Signal et des Images, 2022 | | 2022 |
| Advanced Random Matrix Methods for Machine Learning M Tiomoko Université Paris-Saclay, 2021 | | 2021 |
| Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective (Supplementary Material) MEA Seddik, M Tiomoko, A Decurninge, M Panov, M Guillaud | | |
| Estimation statistique d’une large famille de distances entre matrices de covariance dans le régime des grandes matrices aléatoires M TIOMOKO, R COUILLET | | |
