RPTU researchers achieve exceptional publication success in 2025
- Prof. Dr. Marius Kloft
RPTU researchers achieve exceptional publication success in 2025
Researchers from the Machine Learning Chair at RPTU Kaiserslautern-Landau can look back on an exceptionally successful year in 2025. Two papers were accepted at NeurIPS, the world's leading AI conference – one of the ten most influential scientific publications across all disciplines according to Google Scholar Metrics, comparable to Science and Nature. These successes underscore the international visibility and excellence of AI research in Rhineland-Palatinate.
The publication "NoBOOM: Chemical Process Datasets for Industrial Anomaly Detection,“ a key contribution by the DFG research group FOR 5359 ”Deep Learning on Sparse Chemical Process Data." The project provides the first comprehensive, realistic data basis for AI-supported fault detection in industrial processes – a milestone for research into safe, data-driven production systems. NoBOOM includes both data from industry and datasets from our cooperation partners in chemical process engineering in Kaiserslautern and Munich. This close integration of industrial practice and academic research creates an open, high-quality basis for future work in the field of anomaly detection and process optimization.
The second NeurIPS publication, “Mitigating Spurious Features in Contrastive Learning with Spectral Regularization,” as well as an article in the renowned journal IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), also demonstrate the methodological excellence of RPTU research. The latter article develops new methods of self-monitored anomaly detection that enable machines to independently identify patterns in data and robustly detect deviations – a key step toward explainable and trustworthy AI.
Furthermore, the strength of AI research in Rhineland-Palatinate is evident in its interdisciplinary applications: In the journal Radiology, RPTU researchers, together with international partners, present an AI system for the early detection of breast cancer that applies anomaly detection to medical image data. In Computers & Chemical Engineering, they also demonstrate how large language models can solve complex thermodynamic tasks – an example of the connection between AI and engineering science.
With 24 scientific publications in 2025, including contributions to leading international conferences and journals, the Chair of Machine Learning at RPTU impressively underscores its excellent research performance.
- D. Wagner, F. Hartung, J. Arweiler, A. Muraleedharan, I. Jungjohann, A. Nair, S. Reithermann, R. Schulz, M. Bortz, D. Neider, H. Leitte, J. Pfeffinger, S. Mandt, S. Fellenz, T. Katz, F. Jirasek, J. Burger, H. Hasse, and M. Kloft. NoBOOM: Chemical Process Datasets for Industrial Anomaly Detection.
Advances in Neural Information Processing Systems (NeurIPS) 39, (to appear) 2025. - N. Ghanooni, D. Wagner, W. Mustafa, S. Fellenz, A. W. Lin, and M. Kloft. Mitigating Spurious Features in Contrastive Learning with Spectral Regularization.
Advances in Neural Information Processing Systems (NeurIPS) 39, (to appear) 2025. - C. Qiu, M. Kloft, S. Mandt, and M. Rudolph. Self-supervised Anomaly Detection with Neural Transformations.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 47:3, 2170-2185, 2025. - M. Nagda, P. Ostheimer, and S. Fellenz. Tethering Broken Themes: Aligning Neural Topic Models with Labels and Authors.
Findings of the Association for Computational Linguistics: NAACL, 2025. - L. Manduchi, K. Pandey, R. Bamler, R. Cotterell, S. Däubener, S. Fellenz, A. Fischer, T. Gärtner, M. Kirchler, M. Kloft, Y. Li, C. Lippert, G. de Melo, E. Nalisnick, B. Ommer, R. Ranganath, M. Rudolph, K. Ullrich, G. van den Broeck, J. Vogt, Y. Wang, F. Wenzel, F. Wood, S. Mandt, and V. Fortuin. On the Challenges and Opportunities in Generative AI.
Transactions on Machine Learning Research (TMLR), 2025. - B. Franks, M. Eliasof, S. Cantürk, G. Wolf, C.-B. Schönlieb, S. Fellenz, and M. Kloft. Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings.
Transactions on Machine Learning Research (TMLR), 2025. - N. Ghanooni, W. Mustafa, Y. Lei, A. Lin, and M. Kloft. Generalization Bounds with Logarithmic Negative-Sample Dependence for Adversarial Contrastive Learning.
Transactions on Machine Learning Research (TMLR), 2025. - K. Bykov, M. M.-C. Höhne, A. Creosteanu, K.-R. Müller, F. Klauschen, S. Nakajima, and M. Kloft. Explaining Bayesian Neural Networks.
Transactions on Machine Learning Research (TMLR), 2025. - P. Wang, Y. Lei, M. Kloft, and Y. Ying. Optimal Utility Bounds for Differentially Private Gradient Descent in Three-Layer Neural Networks.
Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), (to appear) 2025. - F. Oviedo, A. S. Kazerouni, P. Liznerski, Y. Xu, M. Hirano, R. A. Vandermeulen, M. Kloft, E. Blum, A. M. Alessio, C. I. Li, W. B. Weeks, R. Dodhia, J. L. Ferres, H. Rahbar, and S. C. Partridge. Cancer detection in breast MRI screening via explainable artificial intelligence anomaly detection.
Radiology 316(1), 2025. - R. Loubet, P. Zittlau, L. Vollmer, M. Hoffmann, S. Fellenz, F. Jirasek, H. Leitte, and H. Hasse. Using large language models for solving textbook-style thermodynamic problems.
Computers & Chemical Engineering 204, 2025. - M. Hussong, P. Ruediger-Flore, M. Klar, M. Kloft, J. Aurich. Selection of manufacturing processes using graph neural networks.
Journal of Manufacturing Systems, 80:176-193, 2025. - S. Varshneya, M. Schürmann, P. Liznerski, M. C. Ahuja, J. C. Aurich, S. Fellenz, and M. Kloft. Anomaly-driven Reinforcement Learning.
Proceedings of the 3rd AAAI Workshop on Automated Spatial and Temporal Anomaly Detection (ASTAD) 2026. - J. Abijuru, M. Nagda, P. Ostheimer, S. Aurich, M. Kloft, and S. Fellenz. Physics-Informed Residual Flows.
Proceedings of the EurIPS Workshop on Differentiable Systems and Scientific Machine Learning 2025. - P. Ostheimer, M. Nagda, A. Balinskyy, J. Radig, C. Herrmann, S. Mandt, M. Kloft, and S. Fellenz. Sparse Data Diffusion for Scientific Simulations in Biology and Physics.
Proceedings of the EurIPS Workshop on Machine Learning For Simulations In Biology And Chemistry (SIMBIOCHEM) 2025. - M. Nagda, J. Abijuru, P. Ostheimer, J. C. Aurich, S. Mandt, M. Kloft, and S. Fellenz. Autoregressive PINNs for Time-Dependent PDEs.
Proceedings of the EurIPS Workshop Differentiable Systems and Scientific Machine Learning 2025. - N. Ghanooni, D. Wagner, W. Mustafa, A. Lin, S. Fellenz, M. Kloft. Spectral Dynamics of Contrastive Learning with Spurious Features.
Proceedings of the ICML 2025 Workshop on High-dimensional Learning Dynamics (HiLD), 2025. - P. Ostheimer, M.Kloft, and S. Fellenz. Challenging Assumptions in Learning Generic Text Style Embeddings.
Proceedings of the Sixth Workshop on Insights from Negative Results in NLP at NAACL-HLT, 2025. - L. Bobojonova, A. Akhundjanova, P. Ostheimer, and S. Fellenz. BBPOS: BERT-based Part-of-Speech Tagging for Uzbek.
Proceedings of the First Workshop on Language Models for Low-Resource Languages (LoResLM) at COLING, 2025. - C. James, W. Mustafa, M. Kloft, and S. Fellenz. Continual Neural Topic Model.
Proceedings of the NeurIPS 2025 WiML Workshop, 2025. - M. Nagda, P. Ostheimer, J. Arweiler, I. Jungjohann, J. Werner, D. Wagner, A. Muraleedharan, P. Jafari, J. Schmid, F. Jirasek, J. Burger, M. Bortz, H. Hasse, S. Mandt, M. Kloft, and S. Fellenz. Style Transfer for High‑Fidelity Time Series Augmentation.
Proceedings of the ECML Workshop on Synthetic Data for AI Trustworthiness and Evolution (SynDAiTE), 2025. - W. Li, W. Mustafa, R. Devidze, M. Kloft, and S. Fellenz. Inference-Time Value Alignment in Offline Reinforcement Learning: Leveraging LLMs for Reward and Ethical Guidance.
The 5th Wordplay: When Language Meets Games (EMNLP 2025 Workshop), 2025. - W. Li, W. Mustafa, M. Monteiro, P. Wang, M. Kloft, and S. Fellenz. TORA: Train Once, Realign Anytime for Offline Multi-Objective Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence, to appear.
- P. Liznerski, S. Varshneya, E. Calikus, P. Wang, A. Bartscher, S. Vollmer, S. Fellenz, and M. Kloft. Reimagining Anomalies: What If Anomalies Were Normal? Proceedings of the AAAI Conference on Artificial Intelligence, to appear.
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10.11.2025 - D. Wagner, F. Hartung, J. Arweiler, A. Muraleedharan, I. Jungjohann, A. Nair, S. Reithermann, R. Schulz, M. Bortz, D. Neider, H. Leitte, J. Pfeffinger, S. Mandt, S. Fellenz, T. Katz, F. Jirasek, J. Burger, H. Hasse, and M. Kloft. NoBOOM: Chemical Process Datasets for Industrial Anomaly Detection.