From the 1 of 16 linked papers with an AI index.
1 citations · 1 across the 9 of their papers we have counts for
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Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization
Chiheb Yaakoubi, Cosme Louart, Malik Tiomoko +1
We study high-dimensional convex empirical risk minimization (ERM) under general non-Gaussian data designs. By heuristically extending the Convex Gaussian Min-Max Theorem (CGMT) to…
-TCAV: A Unified Framework for Testing with Concept Activation Vectors
Ekkehard Schnoor, Jawher Said, Malik Tiomoko +2
Concept Activation Vectors (CAVs) are a fundamental tool for concept-based explainability in deep learning, yet their practical utility is limited by statistical instability. We an…
High-Dimensional Analysis of Bootstrap Ensemble Classifiers
Malik Tiomoko, Hamza Cherkaoui, Mohamed El Amine Seddik +3
Bootstrap methods have long been the cornerstone of ensemble learning in machine learning. This paper presents a theoretical analysis of bootstrap techniques applied to the Least S…
Concept activation vectors: a unifying view and adversarial attacks
Ekkehard Schnoor, Malik Tiomoko, Jawher Said +2
Concept Activation Vectors (CAVs) are a tool from explainable AI, offering a promising approach for understanding how human-understandable concepts are encoded in a model's latent…
Incorporating priors in learning: a random matrix study under a teacher-student framework
Malik Tiomoko, Ekkehard Schnoor
Regularized linear regression is central to machine learning, yet its high-dimensional behavior with informative priors remains poorly understood. We provide the first exact asympt…
A Random Matrix Perspective of Echo State Networks: From Precise Bias--Variance Characterization to Optimal Regularization
Yessin Moakher, Malik Tiomoko, Cosme Louart +1
We present a rigorous asymptotic analysis of Echo State Networks (ESNs) in a teacher student setting with a linear teacher with oracle weights. Leveraging random matrix theory, we…