Ekkehard Schnoor
📖 Bio
Ekkehard Schnoor’s research focuses on the application of high-dimensional probability theory, particularly in establishing performance guarantees within the fields of machine learning and compressive sensing. His primary background is in the classical concept of generalization in machine learning, spanning a spectrum from linear models to deep neural networks, where he has explored both asymptotic and non-asymptotic approaches. Recently, he has developed a keen interest in federated learning, reinforcement learning, and explainability.