2 citations · 3 across the 5 of their papers we have counts for
7 papers
A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization
Ekkehard Schnoor, Malik Tiomoko, Alexander Jung
Federated learning over decentralized networks inherently trades off the suppression of local finite-sample noise through collaboration against the bias that collaboration introduc…
-TCAV: A Unified Framework for Testing with Concept Activation Vectors
Ekkehard Schnoor, Jawher Said, Malik Tiomoko +2
Concept Activation Vectors (CAV) and the Testing with CAVs (TCAV) method are fundamental tools for concept-based explainability in deep learning, yet their practical utility is lim…
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…
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…
Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks
Ekkehard Schnoor, Arash Behboodi, Holger Rauhut
Motivated by the learned iterative soft thresholding algorithm (LISTA), we introduce a general class of neural networks suitable for sparse reconstruction from few linear measureme…
Compressive Sensing and Neural Networks from a Statistical Learning Perspective
Arash Behboodi, Holger Rauhut, Ekkehard Schnoor
Various iterative reconstruction algorithms for inverse problems can be unfolded as neural networks. Empirically, this approach has often led to improved results, but theoretical g…