| 2025 | ICDE | User-Friendly Foundation Model Adapters for Multivariate Time Series Classification. | Romain Ilbert, Vasilii Feofanov, Malik Tiomoko, Ievgen Redko, Themis Palpanas |
| 2024 | ICML | Random matrix theory improved Frchet mean of symmetric positive definite matrices. | Florent Bouchard, Ammar Mian, Malik Tiomoko, Guillaume Ginolhac, Frdric Pascal |
| 2023 | ICLR | Optimizing Spca-based Continual Learning: A Theoretical Approach. | Chunchun Yang, Malik Tiomoko, Zengfu Wang |
| 2023 | ICML | Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. | Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux |
| 2023 | ICML | PCA-based Multi-Task Learning: a Random Matrix Approach. | Malik Tiomoko, Romain Couillet, Frdric Pascal |
| 2023 | UAI | Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective. | Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Guillaud |
| 2022 | ICML | 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 |
| 2021 | ICLR | Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach. | Malik Tiomoko, Hafiz Tiomoko Ali, Romain Couillet |
| 2020 | ICASSP | Large Dimensional Asymptotics of Multi-Task Learning. | Malik Tiomoko, Cosme Louart, Romain Couillet |
| 2019 | ICASSP | Improved Estimation of the Distance between Covariance Matrices. | Malik Tiomoko, Romain Couillet, Eric Moisan, Steeve Zozor |
| 2019 | ICML | Random Matrix Improved Covariance Estimation for a Large Class of Metrics. | Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac |