September 23, 2025
1. Arzu Ahmadova (University of Duisburg-Essen, Germany)
Certifying PINNs: A Posteriori Error Bounds for Dynamical Systems
2. Emma Andersdotter (Umeå University, Sweden)
Steerable NODEs on Homogeneous Spaces
3. Marta Aparicio Rodriguez (Imperial College London, United Kingdom)
Clustering Dynamics in Transformers
4. Wiebke Bartolomaeus (LMU Munich, Germany)
Convergence and Implicit Bias in Gradient Descent Algorithms
5. Jan Benad (Hamburg University of Technology, Germany)
Shared dynamic model aligned hypernetworks for contextual Reinforcement Learning
6. Erik Lien Bolager (Technical University of Munich, Germany)
Data-informed distributions for sampling neural network weights
7. Jonas Bresch (TU Berlin, Germany)
Unsupervised Ground Metric Learning
8. Javier Castro (TU Berlin, Germany)
Thermodynamics-informed Neural Networks (THINNs)
9. Pattarawat Chormai (TU Berlin, Germany)
A Perspective on Relevant Dimensions of Artificial Neural Networks
10. Adwait Datar (Hamburg University of Technology, Germany)
Does the Natural Gradient Really Outperform the Euclidean Gradient?
11. Naima Elosegui Borras (TU Berlin, Germany)
The Rényi divergence in Variational Continual Learning
12. Hannaneh Fahimi (Max Planck Institute for Mathematics in the Sciences, Germany)
IsUMap: Manifold Learning and Data Visualization leveraging Vietoris-Rips Filtrations
13. Timm Faulwasser (Hamburg University of Technology, Germany)
The Optimal Control Perspective on Deep Neural Networks – Early Exits, Insights, and Open Problems
14. Johannes Grün (DESY, Germany)
Near-Field Holo-Tomography Reconstruction Using Neural Radiance Fields
15. Moritz Hollenberg (Hamburg University of Technology, Germany)
Physics-Informed Neural Networks for Heat Flux Reconstruction in Electrical Impedance Tomography
16. Francesco Iafrate (University of Hamburg, Germany)
Learning Continuous-Time Network Dynamics via Network and Sheaf SDEs
17. Jürgen Kampf (University Hospital of Essen, Germany)
Predicting mortality from echocardiographies: A comparison of various machine learning algorithms
18. Gitte Kremling (University of Hamburg, Germany)
Non-asymptotic convergence guarantees for probability flow ODEs under weak log-concavity
19. Manish Krishan Lal (TU Munich, Germany)
From blocks to manifolds: Neural inference as an L2 projection
20. Aleksei Kroshnin (WIAS, Berlin, Germany)
On complexity of accelerated gradient methods for optimizing locally smooth convex functions
21. Sara-Viola Kuntz (TU Munich, Germany)
The Influence of the Network Architecture on the Universal Approximation Capability
22. Jonghyeon Lee (California Institute of Technology, United States)
KROM: Kernelized Reduced Order Model
23. Max Lewerenz (University of Hamburg, Germany)
Kernel-Based Reconstruction in Magnetic Particle Imaging
24. Jannis Lübsen (Hamburg University of Technology, Germany)
Accelerating Safe Bayesian Optimization with Simulations
25. Yannick Lunk (Universität Würzburg, Germany)
Sparse training for neural networks based on linearized Bregman iterations
26. Floor Maarschalkerwaart (University of Twente, Netherlands)
Perturbation-Aware Distributionally Robust Optimization for Inverse Problems
27. Gregor Maier (University of Bonn, Germany)
The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
28. Md Rasel Mandol (National Institute of Technology, India)
Efficient Medical Image Segmentation on Edge Devices
29. Álvaro Márquez (Universidad de Chile, Chile)
Construction of stellar atmospheres models throught the use of PINNs
30. Eloi Martinet (JMU Würzburg, Germany)
Meshless Shape Optimization using Neural Networks and Partial Differential Equations on Graphs
31. Hannes Matt (Katholische Universität Eichstätt-Ingolstadt, Germany)
Linear regression with overparameterized linear neural networks: Tight upper and lower bounds for implicit ℓ1-regularization
32. Philipp Misof (Chalmers University of Technology, Sweden)
Equivariant Neural Tangent Kernels
33. Ali Mohaddes (University of Hamburg, Germany)
Signature Lasso for Fractional Brownian Motion Processes
34. Fabio Schlindwein (Heidelberg University, Germany)
Vector Bundles in Data Science
