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Malik Tiomoko

Random matrix improved covariance estimation for a large class of metrics*
2020 · Malik Tiomoko, Florent Bouchard, et al. · Journal of Statistical Mechanics Theory and Experiment · 10
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Random matrix-improved estimation of covariance matrix distances
2019 · Romain Couillet, Malik Tiomoko, et al. · Journal of Multivariate Analysis · 10
Random Matrix-Improved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions
2019 · Malik Tiomoko, Romain Couillet · European Signal Processing Conference (EUSIPCO) · 5
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Random Matrix Improved Covariance Estimation for a Large Class of Metrics
2019 · Tiomoko, Malik, Bouchard, Florent, et al. · HAL · 5
Improved Estimation of the Distance between Covariance Matrices
2019 · Malik Tiomoko, Romain Couillet, et al. · IEEE International Conference on Acoustics Speech and Signal Processing · 5
Large Dimensional Analysis and Improvement of Multi Task Learning
2020 · Tiomoko, Malik, Couillet, Romain, et al. · arXiv (Cornell University) · 4
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Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
2024 · Romain Ilbert, Malik Tiomoko, et al. · neural information processing systems · 3
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Large Dimensional Asymptotics of Multi-Task Learning
2020 · Malik Tiomoko, Cosme Louart, et al. · IEEE International Conference on Acoustics Speech and Signal Processing · 3
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
2023 · Feofanov, Vasilii, Tiomoko, Malik, et al. · arXiv (Cornell University) · 2
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Estimation of Covariance Matrix Distances in the High Dimension Low Sample Size Regime
2019 · Malik Tiomoko, Romain Couillet · IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing · 2
Breaking the Barriers of Text-Hungry and Audio-Deficient AI
2025 · Tembine, Hamidou, Bamia, Issa, et al. · arXiv (Cornell University) · 1
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Random matrix theory improved Fréchet mean of symmetric positive definite matrices
2024 · Bouchard, Florent, Mian, Ammar, et al. · arXiv (Cornell University) · 1
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PCA-based Multi Task Learning: a Random Matrix Approach
2021 · Tiomoko, Malik, Couillet, Romain, et al. · arXiv (Cornell University) · 1
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Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach
2021 · Tiomoko, Malik, Tiomoko, Hafiz, et al. · HAL · 1
On the Generalization Gap of Streaming Federated Learning with Markovian Data
2026 · Kavian, Masoud, Sefidgaran, Milad, et al. · arXiv (Cornell University)
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A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization
2026 · Schnoor, Ekkehard, Tiomoko, Malik, et al. · arXiv (Cornell University)
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Instance-Adaptive Prompts as Context for Time-Series Foundation Models
2026 · Xiao, Zehao, Xie, Shifeng, et al. · arXiv (Cornell University)
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LLM-Generated Feature Pools for Time Series Anomaly Detection
2026 · Hili, Youssef Attia El, Tiomoko, Malik, et al. · arXiv (Cornell University)
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FlowTSFM: Turning Encoder Depth into Quantile Transport
2026 · Abdessalem, Bahaeddine, Xie, Shifeng, et al. · arXiv (Cornell University)
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Tabby: An Open Pretraining Recipe for Time Series Foundation Models
2026 · Xie, Shifeng, Abdessalem, Bahaeddine, et al. · arXiv (Cornell University)
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EvoCause: LLM-Guided Evolution of Causal Graphs for Root Cause Analysis
2026 · Zan, Lei, Zhang, Keli, et al. · arXiv (Cornell University)
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Post-Training in Time Series Foundation Models: A Unifying Framework
2026 · Xie, Shifeng, Odonnat, Ambroise, et al. · arXiv (Cornell University)
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$α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
2026 · Schnoor, Ekkehard, Said, Jawher, et al. · arXiv (Cornell University)
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Concept Activation Vectors: A Unifying View and Adversarial Attacks
2026 · Ekkehard Schnoor, Malik Tiomoko, et al. · IEEE International Conference on Acoustics Speech and Signal Processing
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A Random Matrix Perspective of Echo State Networks: From Precise Bias-Variance Characterization to Optimal Regularization
2026 · Yessin Moahker, Malik Tiomoko, et al. · IEEE International Conference on Acoustics Speech and Signal Processing
Works count: 47
Citation count: 54
H-index: 5
I10-index: 2
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