Tiomoko, Malik

10 publications

NeurIPS 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

NeurIPSW 2024 Enhancing Multivariate Time Series Forecasting via Multi-Task Learning and Random Matrix Theory Romain Ilbert, Malik Tiomoko, Cosme Louart, Vasilii Feofanov, Themis Palpanas, Ievgen Redko

ICML 2024 Random Matrix Theory Improved Fréchet Mean of Symmetric Positive Definite Matrices Florent Bouchard, Ammar Mian, Malik Tiomoko, Guillaume Ginolhac, Frederic Pascal

UAI 2023 Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Gauillaud

ICLR 2023 Optimizing Spca-Based Continual Learning: A Theoretical Approach Chunchun Yang, Malik Tiomoko, Zengfu Wang

ICML 2023 PCA-Based Multi-Task Learning: A Random Matrix Approach Malik Tiomoko, Romain Couillet, Frederic Pascal

ICML 2023 Random Matrix Analysis to Balance Between Supervised and Unsupervised Learning Under the Low Density Separation Assumption Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux

ICML 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

ICLR 2021 Deciphering and Optimizing Multi-Task Learning: A Random Matrix Approach Malik Tiomoko, Hafiz Tiomoko Ali, Romain Couillet

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