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Malik Tiomoko
Malik Tiomoko
Senior Research Engineer, Huawei Noah's Ark Lab
Adresse e-mail validée de huawei.com - Page d'accueil
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Année
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
142019
Random matrix-improved estimation of covariance matrix distances
R Couillet, M Tiomoko, S Zozor, E Moisan
Journal of Multivariate Analysis 174, 104531, 2019
122019
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
102021
PCA-based multi-task learning: a random matrix approach
M Tiomoko, R Couillet, F Pascal
International Conference on Machine Learning, 34280-34300, 2023
72023
Large dimensional analysis and improvement of multi task learning
M Tiomoko, R Couillet, H Tiomoko
arXiv preprint arXiv:2009.01591, 2020
72020
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
62019
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
62019
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
42023
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
32022
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
32019
Large dimensional asymptotics of multi-task learning
M Tiomoko, C Louart, R Couillet
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020
22020
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
12023
Optimizing Spca-based Continual Learning: A Theoretical Approach
C Yang, M Tiomoko, Z Wang
The Eleventh International Conference on Learning Representations, 2022
12022
Multi-task learning on the edge: cost-efficiency and theoretical optimality
S Fakhry, R Couillet, M Tiomoko
arXiv preprint arXiv:2110.04639, 2021
12021
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
Random matrix theory improved Fr\'echet mean of symmetric positive definite matrices
F Bouchard, A Mian, M Tiomoko, G Ginolhac, F Pascal
arXiv preprint arXiv:2405.06558, 2024
2024
Apprentissage multitâche en grande dimension: classification basée sur les covariances
C Doz, M Tiomoko, C Ren, JP Ovarlez
GRETSI 2023, 2023
2023
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
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