Malik Tiomoko

According to our database, Malik Tiomoko authored at least 17 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

User-friendly Foundation Model Adapters for Multivariate Time Series Classification.

Vasilii Feofanov, Romain Ilbert, Malik Tiomoko, Themis Palpanas, Ievgen Redko

CoRR, 2024

Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting.

Romain Ilbert, Malik Tiomoko, Cosme Louart, Ambroise Odonnat, Vasilii Feofanov, Themis Palpanas, Ievgen Redko

CoRR, 2024

Random matrix theory improved Fréchet mean of symmetric positive definite matrices.

Florent Bouchard, Ammar Mian, Malik Tiomoko, Guillaume Ginolhac, Frédéric Pascal

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective.

Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Guillaud

Proceedings of the Uncertainty in Artificial Intelligence, 2023

PCA-based Multi-Task Learning: a Random Matrix Approach.

Malik Tiomoko, Romain Couillet, Frédéric Pascal

Proceedings of the International Conference on Machine Learning, 2023

Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption.

Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux

Proceedings of the International Conference on Machine Learning, 2023

Optimizing Spca-based Continual Learning: A Theoretical Approach.

Chunchun Yang, Malik Tiomoko, Zengfu Wang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm.

Malik Tiomoko, Ekkehard Schnoor, Mohamed El Amine Seddik, Igor Colin, Aladin Virmaux

Proceedings of the International Conference on Machine Learning, 2022

2021

Advanced Random Matrix Methods for Machine Learning. (Méthodes avancées de la théorie des matrices aléatoires pour l'apprentissage automatique).

Malik Tiomoko

PhD thesis, 2021

Multi-task learning on the edge: cost-efficiency and theoretical optimality.

Sami Fakhry, Romain Couillet, Malik Tiomoko

CoRR, 2021

Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach.

Malik Tiomoko, Hafiz Tiomoko Ali, Romain Couillet

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Large Dimensional Asymptotics of Multi-Task Learning.

Malik Tiomoko, Cosme Louart, Romain Couillet

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019

Random matrix-improved estimation of covariance matrix distances.

Romain Couillet, Malik Tiomoko, Steeve Zozor, Eric Moisan

J. Multivar. Anal., 2019

Random Matrix Improved Covariance Estimation for a Large Class of Metrics.

Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac

Proceedings of the 36th International Conference on Machine Learning, 2019

Improved Estimation of the Distance between Covariance Matrices.

Malik Tiomoko, Romain Couillet, Eric Moisan, Steeve Zozor

Proceedings of the IEEE International Conference on Acoustics, 2019

Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions.

Malik Tiomoko, Romain Couillet

Proceedings of the 27th European Signal Processing Conference, 2019

Estimation of Covariance Matrix Distances in the High Dimension Low Sample Size Regime.

Malik Tiomoko, Romain Couillet

Proceedings of the 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2019