Maximilian Fleissner

Maximilian Fleissner is affiliated with the Technical University of Munich's School of Computation, Information and Technology, specifically within the Department of Theoretical Foundations of Artificial Intelligence (Informatik 7). His research focuses on Statistical Learning Theory, Kernel Methods, and Explainable Machine Learning. He explores theoretical guarantees in machine learning, particularly in autoencoders, self-supervised frameworks, and interpretable clustering techniques.

Recent publications (2024–2025) emphasize foundational topics like generalization in denoising autoencoders, probabilistic models for non-contrastive learning, and infinite-width limits of neural networks. His work bridges theory and application, with a focus on kernel-based methods and decision-tree-driven explanations for clustering.

No scientific awards or grants are explicitly mentioned in the provided text. Advising records are not listed. His department is part of the larger Faculty of Informatics at TUM.