Publications

These publications will be moved to the publication list as they are added to our repository

• A. Fono, M. Singh, E. Araya, P. Petersen, H. Boche, G. Kutyniok. Mathematical Foundations of Spiking Neural Networks. IEEE Signal Processing Magazine, Special Issue on The Mathematics of Deep Learning, 2025 (to appear). (arXiv:2503.02013)
• H. Boche, A. Fono, and G. Kutyniok. “Turing meets Moore-Penrose: Computing the Pseudoinversen on Turing Machines”. In: IWOTA 2024 Conference Proceedings. Ed. by M. Iliopoulou, B. Lemmens, A. Loureiro, M. Marletta, and I. Wood. to appear. 2025.
• N. Bar, M. Seleznova, Y. Alexander, G. Kutyniok, R. Giryes. Revisiting Glorot Initialization for Long-Range Linear Recurrences. (arXiv:2505.19827)
• C. Kneissl, C. Bülte, P. Scholl, G. Kutyniok. "Uncertainty-aware diffusion models for probabilistic regression". The Epistemic Intelligence in Machine Learning workshop at the EURIPS 2025
• Kaissis, G., Kolek, S., Balle, B., Hayes, J. and Rueckert, D., 2024, July. Beyond the Calibration Point: Mechanism Comparison in Differential Privacy. In International Conference on Machine Learning (pp. 22840-22860). PMLR.
• S. Kolek, A. Chattopadhyay, K. Chan, H. Andrade-Loarca, G. Kutyniok, R. Vidal. Learning Interpretable Queries for Explainable Image Classification with Information Pursuit. ICCV 2025 (arXiv:2312.11548).
• J. von Berg, A. Fono, M. Datres , S. Maskey, G. Kutyniok. The Price of Robustness: Stable Classifiers Need Overparameterization. ICML 2025 Workshop “3rd Workshop on High-dimensional Learning Dynamics (HiLD)”. (https://openreview.net/forum?id=rmFYS3vTwR)
• E. Araya, M. Cucuringu, H. Tyagi. Dynamic angular synchronization under smoothness constraints, Journal of Machine Learning Research (JMLR), 26(79):1--45 (2025)
• C. Bülte, S. Maskey, P. Scholl, J. von Berg, G. Kutyniok. Graph Neural Networks For Enhancing Ensemble Forecasts Of Extreme Rainfall. ICLR 2025 Workshop “Tackling Climate Change with Machine Learning”. (arXiv:2504.05471)
• A. Fono, H. Boche, G. Kutyniok. How to realize efficient Spiking Neural Networks?. AAAI 2026 Workshop on 'Foretell of Future AI from Mathematical Foundation' (Math4AI)
• C. Bülte, Y. Sale, T. Löhr, P. Hofman, G. Kutyniok, E. Hüllermeier. A Formal Assessment of Uncertainty Measures in Regression. EIML@EurIPS 2025 Workshop Epistemic Intelligence in Machine Learning
• L. Yang, Y. Bai, G. Eskandar, F. Shen, M. Altillawi, D. Chen, S. Majumder, S. Liu, G. Kutyniok, A. Valada. RoboEnvision: A Long-Horizon Video Generation Model for Multi-Task Robot Manipulation. IROS. 2025.(arXiv:2506.22007)
• W. Samek, U. Schmidt, J. Hoffart, D. Keim, G. Kutyniok, P. Schlunder. Nachvollziehbare KI. Erklären, für wen, was und wofür. Plattform Lernende Systeme, 2025.
• G. Kutyniok. How Can Reliability of Artificial Intelligence Be Ensured? In: Harvard Data Science Review 7, 2025. [link]
• H. Boche, A. Fono, and G. Kutyniok. Fundamentale Grenzen der künstlichen Intelligenz aus mathematischer Sicht. In: Sammelband “Grenzen Künstlicher Intelligenz“ (Markus Maier/Benjamin Rathgeber (Hrsg.), Kohlhammer-Gruppe, 2025.
• W.Samek, U. Schmid, J. Hoffart, D. Keim, G. Kutyniok, P. Schlunder, Whitepaper “Nach vollziehbare KI. Erklären, für wen, was und wofür“, Plattform Lernende Systeme, 2025.
• S. Brüggenwirth, A. Burchard, T. Fingscheidt, H. Hoos, K. Illgner, H. Junklewitz, A. Kaup, K. von Knop, J. Köhler, G. Kutyniok, R. Martin, D. Kolossa, S. Möller, R. Schlüter, D. Thulke, V. Schmitt, I. Siegert, V. Ziegler, Large Language Models are Transformers in Artificial Intelligence, Industry, Education, and Society, Positionspapier der VDE ITG, 2025.
• H. Boche, A. Fono, and G. Kutyniok. “HPC und KI - Wie erzielen wir Vertrauenswürdigkeit und reduzieren den Energieverbrauch?” In: Physik: Erkenntnisse und Perspektiven. Deutsche Physikalische Gesellschaft e.V., 2025
• I. Butz, H. Andrade-Loarca, A. Schiavi, V. Patera, O. Öktem, G. Kutyniok, K. Parodi, C. Gianoli. Investigation of data-driven stopping power calibration of treatment planning x-ray CT from simulated sparse-view proton radiographies. Phys. Med. Biol. (2025) ( 10.1088/1361-6560/ae2418 )

2025

• V. Fojtik, M. Matveev, H. Chou, G. Kutyniok, J. Maly. Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization. (arXiv:2505.21423)
• M. Seleznova, H. Chou, M. Verdun, G. Kutyniok. GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection. (arXiv:2505.16017)
• J. Lee and G. Kutyniok. Expressivity of Deep Neural Networks. (pdf)
• Sustainable AI: Mathematical Foundations of Spiking Neural Networks. A. Fono, M. Singh, E. Araya, P. Petersen, H. Boche, G. Kutyniok. (arXiv:2503.02013v1)
• C. Bülte, Y. Sale, T. Löhr, P. Hofman, G. Kutyniok, E. Hüllermeier. An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression. (arXiv:2504.18433)
• P. Scholl, A. Dietrich, S. Wolf, J. Lee, A. Schäffer, G. Kutyniok, M. Iskandar. Interpretable Robotic Friction Learning via Symbolic Regression. (arXiv:2505.13186)
• Z. Shumaylov, P. Zaika, P. Scholl, G. Kutyniok, L. Horesh, C. Schönlieb. When is a System Discoverable from Data? Discovery Requires Chaos. (https://arxiv.org abs/2511.08860)
• C. Kneissl, C. Bülte, P. Scholl, G. Kutyniok. Improved probabilistic regression using diffusion models.(arXiv:2510.04583)
• J. Suarez Cardona, H. Boche, G. Kutyniok. A Variational Framework for the Algorithmic Complexity of PDE Solutions. (arXiv:2510.21290)
• E. Araya, M. Datres, G. Kutyniok. Random Spiking Neural Networks are Stable and Spectrally Simple. (https://arxiv.org/abs/2511.00904)
• C. Bülte, Y. Sale, G. Kutyniok, E. Hüllermeier. Uncertainty Quantification for Regression: A Unified Framework based on kernel scores (arXiv:2510.25599)

2024

• P. Scholl, A. Bacho, H. Boche, G. Kutyniok. Symbolic Recovery of Differential Equations: The Identifiability Problem (arXiv:2210.08342)

2023

• M. Singh, A. Fono, G. Kutyniok. Expressivity of Spiking Neural Networks (arXiv:2308.08218)
• H. Andrade-Loarca, J. Hege, A. Bacho, G. Kutyniok. PoissonNet: Resolution-Agnostic 3D Shape Reconstruction using Fourier Neural Operators (arXiv:2308.01766)
• A. Bacho, H. Boche, G. Kutyniok. Reliable AI: Does the Next Generation Require
  Quantum Computing? (arXiv:2307.01301v1)
• Hung-Hsu Chou, Johannes Maly, Claudio Mayrink Verdun. Non-negative Least Squares via Overparametrization (arXiv:2207.08437)