6 ICML papers for the ML Group at RPTU in Kaiserslautern – 10 for the Department of Computer Science in total
- Prof. Dr. Marius Kloft
6 ICML papers for the ML Group at RPTU in Kaiserslautern – 10 for the Department of Computer Science in total
The Machine Learning Group at RPTU Kaiserslautern-Landau, led by Prof. Dr. Marius Kloft and Jun. -Prof. Dr. Sophie Fellenz
has successfully placed 6 papers at the International Conference on Machine Learning (ICML) 2026.
Alongside NeurIPS and ICLR, ICML is one of the world’s three leading AI conferences; this year’s acceptance rate was approximately 26%, with a record number of 24,371 submissions.
The accepted papers cover a remarkably broad spectrum: from differential privacy on Kolmogorov–Arnold networks
to physics-informed neural networks and diffusion models, all the way to visual anomaly detection and the optimization of large language models.Beyond that, the Department of Computer Science at RPTU is also performing remarkably well.
2 of the 6 papers from the ML Group were produced in collaboration with Prof. Dr. Sebastian Vollmer,
who is also involved in 2 additional accepted papers.Prof. Dr. Anthony Lin’s research group has 2 further acceptances.
This brings the total to at least 10 ICML papers from the Department of Computer Science at RPTU in Kaiserslautern,
a clear indication of Kaiserslautern’s impact in international AI research.ICML 2026 will take place in Seoul from July 6 to 11, 2026.
- J. Abijuru, M. Nagda, P. Ostheimer, J. Tauberschmidt, S. Vollmer, S. Mandt, M. Kloft, and S. Fellenz. Heavy-tailed Physics-Informed Neural Networks.
Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026. - P. Wang, J. Zhou, P. Liznerski, and M. Kloft. Optimization, Generalization and Differential Privacy Bounds for Gradient Descent on Kolmogorov–Arnold Networks.
Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026. - M. Monteiro, W. Li, P. Wang, M. Kloft, and S. Fellenz. Landmark-Guided Policy Optimization for Multi-Objective Language Model Selection.
Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026. - J. Abijuru, M. Nagda, P. Ostheimer, S. Vollmer, M. Kloft, and S. Fellenz. Physics-Informed Residual Flows.
Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026. - N. Iqbal, D. Wagner, P. Liznerski, N. Syed, S. Fellenz, N. Martinel, and M. Kloft. Formally Exploring Visual Anomaly Detection Evaluation Metrics.
Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026. - P. Ostheimer, M. Nagda, A. Balinskyy, G. Rodrigues, J. Radig, C. Herrmann, S. Mandt, M. Kloft, and S. Fellenz. Skipping the Zeros in Diffusion Models for Sparse Data Generation.
Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026. - A. Warrier, D. Nguyen, M. Naim, M. Jain, Y. Liang, K. Schroeder, C. Yang, J. Tenenbaum, S. Vollmer, K. Ellis, and Z. Tavares. Benchmarking World-Model Learning with Environment-Level Queries.
Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026. - S. Thies, V. Bengs, T. Kaufmann, S. Vollmer, and E. Hüllermeier. Calibrated Preference Learning: The Case of Label Ranking.
Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026. - W. Merrill, H. Jiang, Y. Li, A. Lin, and A. Sabharwal. Why Are Linear RNNs More Parallelizable?
Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026. - A. Lin, P. Bergsträßer, G. Zetzsche, A. Yang, and D. Chiang. Length Generalization Bounds for Transformers.
Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2026.
For further information: Steffen Reithermann, steffen.reithermann@cs.rptu.de
https://ml.cs.rptu.de/#publications
04.05.2026 - J. Abijuru, M. Nagda, P. Ostheimer, J. Tauberschmidt, S. Vollmer, S. Mandt, M. Kloft, and S. Fellenz. Heavy-tailed Physics-Informed Neural Networks.