Estimation of Covariance Matrix Distances in the High Dimension Low Sample Size Regime. - Malik Tiomoko; Romain Couillet (CAMSAP, 2019)

Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions. - Malik Tiomoko; Romain Couillet (EUSIPCO, 2019)

Improved Estimation of the Distance between Covariance Matrices. - Malik Tiomoko; Romain Couillet; Eric Moisan; Steeve Zozor (ICASSP, 2019)

Random Matrix Improved Covariance Estimation for a Large Class of Metrics. - Malik Tiomoko; Romain Couillet; Florent Bouchard; Guillaume Ginolhac (ICML, 2019)

Random matrix-improved estimation of covariance matrix distances. - Romain Couillet; Malik Tiomoko; Steeve Zozor; Eric Moisan (J. Multivar. Anal., 2019)

Large Dimensional Asymptotics of Multi-Task Learning. - Malik Tiomoko; Cosme Louart; Romain Couillet (ICASSP, 2020)

Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach. - Malik Tiomoko; Hafiz Tiomoko Ali; Romain Couillet (ICLR, 2021)

Multi-task learning on the edge: cost-efficiency and theoretical optimality. - Sami Fakhry; Romain Couillet; Malik Tiomoko (CoRR, 2021)

Advanced Random Matrix Methods for Machine Learning. (Méthodes avancées de la théorie des matrices aléatoires pour l'apprentissage automatique). - Malik Tiomoko (2021)

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 (ICML, 2022)

Optimizing Spca-based Continual Learning: A Theoretical Approach. - Chunchun Yang; Malik Tiomoko; Zengfu Wang (ICLR, 2023)

Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. - Vasilii Feofanov; Malik Tiomoko; Aladin Virmaux (ICML, 2023)

PCA-based Multi-Task Learning: a Random Matrix Approach. - Malik Tiomoko; Romain Couillet; Frédéric Pascal 001 (ICML, 2023)

Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective. - Mohamed El Amine Seddik; Malik Tiomoko; Alexis Decurninge; Maxim Panov; Maxime Guillaud (UAI, 2023)

Random matrix theory improved Fréchet mean of symmetric positive definite matrices. - Florent Bouchard; Ammar Mian; Malik Tiomoko; Guillaume Ginolhac; Frédéric Pascal 001 (ICML, 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 (NeurIPS, 2024)

User-Friendly Foundation Model Adapters for Multivariate Time Series Classification. - Romain Ilbert; Vasilii Feofanov; Malik Tiomoko; Ievgen Redko; Themis Palpanas (ICDEW, 2025)

Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification. - Vasilii Feofanov; Songkang Wen; Marius Alonso; Romain Ilbert; Hongbo Guo; Malik Tiomoko; Lujia Pan; Jianfeng Zhang; Ievgen Redko (CoRR, 2025)

High-Dimensional Analysis of Bootstrap Ensemble Classifiers. - Hamza Cherkaoui; Malik Tiomoko; Mohamed El Amine Seddik; Cosme Louart; Ekkehard Schnoor; Balázs Kégl (CoRR, 2025)

Human in the Loop Adaptive Optimization for Improved Time Series Forecasting. - Malik Tiomoko; Hamza Cherkaoui; Giuseppe Paolo; Zhang Yili; Yu Meng; Zhang Keli; Hafiz Tiomoko Ali (CoRR, 2025)

Breaking the Barriers of Text-Hungry and Audio-Deficient AI. - Hamidou Tembine; Issa Bamia; Massa Ndong; Bakary Coulibaly; Oumar Issiaka Traore; Moussa Traore; Moussa Sanogo; Mamadou Eric Sangare; Salif Kante; Daryl Noupa Yongueng; Hafiz Tiomoko Ali; Malik Tiomoko; et al. (CoRR, 2025)