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Malik Tiomoko
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2020 – today
- 2024
- [c12]Florent Bouchard, Ammar Mian, Malik Tiomoko, Guillaume Ginolhac, Frédéric Pascal:
Random matrix theory improved Fréchet mean of symmetric positive definite matrices. ICML 2024 - [i9]Florent Bouchard, Ammar Mian, Malik Tiomoko, Guillaume Ginolhac, Frédéric Pascal:
Random matrix theory improved Fréchet mean of symmetric positive definite matrices. CoRR abs/2405.06558 (2024) - [i8]Romain Ilbert, Malik Tiomoko, Cosme Louart, Ambroise Odonnat, Vasilii Feofanov, Themis Palpanas, Ievgen Redko:
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting. CoRR abs/2406.10327 (2024) - [i7]Vasilii Feofanov, Romain Ilbert, Malik Tiomoko, Themis Palpanas, Ievgen Redko:
User-friendly Foundation Model Adapters for Multivariate Time Series Classification. CoRR abs/2409.12264 (2024) - 2023
- [c11]Chunchun Yang, Malik Tiomoko, Zengfu Wang:
Optimizing Spca-based Continual Learning: A Theoretical Approach. ICLR 2023 - [c10]Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux:
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. ICML 2023: 10008-10033 - [c9]Malik Tiomoko, Romain Couillet, Frédéric Pascal:
PCA-based Multi-Task Learning: a Random Matrix Approach. ICML 2023: 34280-34300 - [c8]Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Guillaud:
Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective. UAI 2023: 1858-1867 - [i6]Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux:
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. CoRR abs/2310.13434 (2023) - 2022
- [c7]Malik Tiomoko, Ekkehard Schnoor, Mohamed El Amine Seddik, Igor Colin, Aladin Virmaux:
Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm. ICML 2022: 21449-21477 - 2021
- [b1]Malik Tiomoko:
Advanced Random Matrix Methods for Machine Learning. (Méthodes avancées de la théorie des matrices aléatoires pour l'apprentissage automatique). University of Paris-Saclay, France, 2021 - [c6]Malik Tiomoko, Hafiz Tiomoko Ali, Romain Couillet:
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach. ICLR 2021 - [i5]Sami Fakhry, Romain Couillet, Malik Tiomoko:
Multi-task learning on the edge: cost-efficiency and theoretical optimality. CoRR abs/2110.04639 (2021) - [i4]Malik Tiomoko, Romain Couillet, Frédéric Pascal:
PCA-based Multi Task Learning: a Random Matrix Approach. CoRR abs/2111.00924 (2021) - 2020
- [c5]Malik Tiomoko, Cosme Louart, Romain Couillet:
Large Dimensional Asymptotics of Multi-Task Learning. ICASSP 2020: 8787-8791
2010 – 2019
- 2019
- [j1]Romain Couillet, Malik Tiomoko, Steeve Zozor, Eric Moisan:
Random matrix-improved estimation of covariance matrix distances. J. Multivar. Anal. 174 (2019) - [c4]Malik Tiomoko, Romain Couillet:
Estimation of Covariance Matrix Distances in the High Dimension Low Sample Size Regime. CAMSAP 2019: 341-345 - [c3]Malik Tiomoko, Romain Couillet:
Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions. EUSIPCO 2019: 1-5 - [c2]Malik Tiomoko, Romain Couillet, Eric Moisan, Steeve Zozor:
Improved Estimation of the Distance between Covariance Matrices. ICASSP 2019: 7445-7449 - [c1]Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac:
Random Matrix Improved Covariance Estimation for a Large Class of Metrics. ICML 2019: 6254-6263 - [i3]Malik Tiomoko, Florent Bouchard, Guillaume Ginolhac, Romain Couillet:
Random Matrix Improved Covariance Estimation for a Large Class of Metrics. CoRR abs/1902.02554 (2019) - [i2]Malik Tiomoko, Romain Couillet:
Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions. CoRR abs/1903.03447 (2019) - 2018
- [i1]Romain Couillet, Malik Tiomoko, Steeve Zozor, Eric Moisan:
Random matrix-improved estimation of covariance matrix distances. CoRR abs/1810.04534 (2018)
Coauthor Index
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