Malik Tiomoko - RankMe

34 papers A* 8A 1Misc 2Journal 19Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Random Matrix Perspective of Echo State Networks: From Precise Bias-Variance Characterization to Optimal Regularization. CoRR Yessin Moakher, Malik Tiomoko, Cosme Louart, Zhenyu Liao
2025	J	`jnl`	Breaking the Barriers of Text-Hungry and Audio-Deficient AI. CoRR 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, Frejus Laleye, Boualem Djehiche, Wesmanegda Elisee Dipama, Idris Baba Saje, Hammid Mohammed Ibrahim, Moumini Sanogo, Marie Coursel Nininahazwe, Abdul-Latif Siita, Haine Mhlongo, Teddy Nelvy Dieu Merci Kouka, Mariam Serine Jeridi, Mutiyamuogo Parfait Mupenge, Lekoueiry Dehah, Abdoul-Aziz Bio Sidi D. Bouko, Wilfried Franceslas Zokoue, Odette Richette Sambila, Alina RS Mbango, Mady Diagouraga, Oumarou Moussa Sanoussi, Gizachew Dessalegn, Mohamed Lamine Samoura, Bintou Laetitia Audrey Coulibaly
2025	J	`jnl`	Concept activation vectors: a unifying view and adversarial attacks. CoRR Ekkehard Schnoor, Malik Tiomoko, Jawher Said, Alex Jung, Wojciech Samek
2025	J	`jnl`	Generalization in Representation Models via Random Matrix Theory: Application to Recurrent Networks. CoRR Yessin Moakher, Malik Tiomoko, Cosme Louart, Zhenyu Liao
2025	J	`jnl`	High-Dimensional Analysis of Bootstrap Ensemble Classifiers. CoRR Hamza Cherkaoui, Malik Tiomoko, Mohamed El Amine Seddik, Cosme Louart, Ekkehard Schnoor, Balázs Kégl
2025	J	`jnl`	Human in the Loop Adaptive Optimization for Improved Time Series Forecasting. CoRR Malik Tiomoko, Hamza Cherkaoui, Giuseppe Paolo, Yili Zhang, Yu Meng, Keli Zhang, Hafiz Tiomoko Ali
2025	J	`jnl`	Incorporating priors in learning: a random matrix study under a teacher-student framework. CoRR Malik Tiomoko, Ekkehard Schnoor
2025	J	`jnl`	LLMs as In-Context Meta-Learners for Model and Hyperparameter Selection. CoRR Youssef Attia El Hili, Albert Thomas, Malik Tiomoko, Abdelhakim Benechehab, Corentin Léger, Corinne Ancourt, Balázs Kégl
2025	J	`jnl`	Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification. CoRR Vasilii Feofanov, Songkang Wen, Marius Alonso, Romain Ilbert, Hongbo Guo, Malik Tiomoko, Lujia Pan, Jianfeng Zhang, Ievgen Redko
2025	—	`conf`	User-Friendly Foundation Model Adapters for Multivariate Time Series Classification. ICDEW Romain Ilbert, Vasilii Feofanov, Malik Tiomoko, Ievgen Redko, Themis Palpanas
2024	A*	`conf`	Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting. NeurIPS Romain Ilbert, Malik Tiomoko, Cosme Louart, Ambroise Odonnat, Vasilii Feofanov, Themis Palpanas, Ievgen Redko
2024	J	`jnl`	Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting. CoRR Romain Ilbert, Malik Tiomoko, Cosme Louart, Ambroise Odonnat, Vasilii Feofanov, Themis Palpanas, Ievgen Redko
2024	A*	`conf`	Random matrix theory improved Fréchet mean of symmetric positive definite matrices. ICML Florent Bouchard, Ammar Mian, Malik Tiomoko, Guillaume Ginolhac, Frédéric Pascal
2024	J	`jnl`	Random matrix theory improved Fréchet mean of symmetric positive definite matrices. CoRR Florent Bouchard, Ammar Mian, Malik Tiomoko, Guillaume Ginolhac, Frédéric Pascal
2024	J	`jnl`	User-friendly Foundation Model Adapters for Multivariate Time Series Classification. CoRR Vasilii Feofanov, Romain Ilbert, Malik Tiomoko, Themis Palpanas, Ievgen Redko
2023	A	`conf`	Learning from Low Rank Tensor Data: A Random Tensor Theory Perspective. UAI Mohamed El Amine Seddik, Malik Tiomoko, Alexis Decurninge, Maxim Panov, Maxime Guillaud
2023	A*	`conf`	Optimizing Spca-based Continual Learning: A Theoretical Approach. ICLR Chunchun Yang, Malik Tiomoko, Zengfu Wang
2023	A*	`conf`	PCA-based Multi-Task Learning: a Random Matrix Approach. ICML Malik Tiomoko, Romain Couillet, Frédéric Pascal
2023	A*	`conf`	Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. ICML Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux
2023	J	`jnl`	Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption. CoRR Vasilii Feofanov, Malik Tiomoko, Aladin Virmaux
2022	A*	`conf`	Deciphering Lasso-based Classification Through a Large Dimensional Analysis of the Iterative Soft-Thresholding Algorithm. ICML Malik Tiomoko, Ekkehard Schnoor, Mohamed El Amine Seddik, Igor Colin, Aladin Virmaux
2021	—	`—`	Advanced Random Matrix Methods for Machine Learning. (Méthodes avancées de la théorie des matrices aléatoires pour l'apprentissage automatique). Malik Tiomoko
2021	A*	`conf`	Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach. ICLR Malik Tiomoko, Hafiz Tiomoko Ali, Romain Couillet
2021	J	`jnl`	Multi-task learning on the edge: cost-efficiency and theoretical optimality. CoRR Sami Fakhry, Romain Couillet, Malik Tiomoko
2021	J	`jnl`	PCA-based Multi Task Learning: a Random Matrix Approach. CoRR Malik Tiomoko, Romain Couillet, Frédéric Pascal
2020	Misc	`conf`	Large Dimensional Asymptotics of Multi-Task Learning. ICASSP Malik Tiomoko, Cosme Louart, Romain Couillet
2019	—	`conf`	Estimation of Covariance Matrix Distances in the High Dimension Low Sample Size Regime. CAMSAP Malik Tiomoko, Romain Couillet
2019	Misc	`conf`	Improved Estimation of the Distance between Covariance Matrices. ICASSP Malik Tiomoko, Romain Couillet, Eric Moisan, Steeve Zozor
2019	A*	`conf`	Random Matrix Improved Covariance Estimation for a Large Class of Metrics. ICML Malik Tiomoko, Romain Couillet, Florent Bouchard, Guillaume Ginolhac
2019	J	`jnl`	Random Matrix Improved Covariance Estimation for a Large Class of Metrics. CoRR Malik Tiomoko, Florent Bouchard, Guillaume Ginolhac, Romain Couillet
2019	—	`conf`	Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions. EUSIPCO Malik Tiomoko, Romain Couillet
2019	J	`jnl`	Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions. CoRR Malik Tiomoko, Romain Couillet
2019	J	`jnl`	Random matrix-improved estimation of covariance matrix distances. J. Multivar. Anal. Romain Couillet, Malik Tiomoko, Steeve Zozor, Eric Moisan
2018	J	`jnl`	Random matrix-improved estimation of covariance matrix distances. CoRR Romain Couillet, Malik Tiomoko, Steeve Zozor, Eric Moisan