Afroze, L., Merkelbach, S., von Enzberg, S., &; Dumitrescu, R. (2024). Domain Knowledge Injection Guidance for Predictive Maintenance. ML4CPS 2023. ML4CPS – Machine Learning For Cyber-Physical Systems, Hamburg.
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Offen, C., &; Ober-Blöbaum, S. (2024). Learning of discrete models of variational PDEs from data. Chaos, 34(1), Article 013104.https://doi.org/10.1063/5.0172287 
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DOI: 10.1063/5.0172287
arXiv: 2308.05082 

Weber, K. S., Schlesinger, S., Lang, A., Straßburger, K., Maalmi, H., Zhu, A., Zaharia, O.-P., Strom, A., Bönhof, G. J., Goletzke, J., Trenkamp, S., Wagner, R., Buyken, A., Lieb, W., Roden, M., Herder, C., Roden, M., Al-Hasani, H., Belgardt, B.,https://doi.org/10.1016/j.numecd.2023.12.026__Wagner, R. (2024). Association of dietary patterns with diabetes-related comorbidities varies among diabetes endotypes. Nutrition, Metabolism and Cardiovascular Diseases.https://doi.org/10.1016/j.numecd.2023.12.026 
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DOI: 10.1016/j.numecd.2023.12.026

Peitz, S., Stenner, J., Chidananda, V., Wallscheid, O., Brunton, S. L., &; Taira, K. (2024). Distributed Control of Partial Differential Equations Using  Convolutional Reinforcement Learning. Physica D: Nonlinear Phenomena, 461, 134096.https://doi.org/10.1016/j.physd.2024.134096 
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DOI: 10.1016/j.physd.2024.134096

Vanita, V., Waidha, A. I., Vasala, S., Puphal, P., Schoch, R., Glatzel, P., Bauer, M., &; Clemens, O. (2024). Insights into the First Multi-Transition-Metal Containing Ruddlesden Popper-Type Cathode for all-solid-state Fluoride Ion Batteries. Journal of Materials Chemistry A.https://doi.org/10.1039/d4ta00704b 
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DOI: 10.1039/d4ta00704b

Otto, S. E., Peitz, S., &; Rowley, C. W. (2024). Learning Bilinear Models of Actuated Koopman Generators from  Partially-Observed Trajectories. SIAM Journal on Applied Dynamical Systems, 23(1), 885–923.https://doi.org/10.1137/22M1523601 
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DOI: 10.1137/22M1523601
arXiv: 2209.09977

Buyken, A., &; Libuda, L. (2024). Ernährung und Alltagsbewältigung - Ein Spannungsfeld für Individuum, Haushalt und Gesellschaft. DGEwissen.
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Hu, L., Habernal, I., Shen, L., &; Wang, D. (2024). Differentially Private Natural Language Models: Recent Advances and Future Directions. In Y. Graham &; M. Purver (Eds.), Findings of the Association for Computational Linguistics: EACL 2024, St. Julian’s, Malta, March 17-22, 2024 (pp. 478–499). Association for Computational Linguistics.
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Igamberdiev, T., Vu, D. N. L., Kuennecke, F., Yu, Z., Holmer, J., &; Habernal, I. (2024). DP-NMT: Scalable Differentially Private Machine Translation. In N. Aletras &; O. De Clercq (Eds.), Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (pp. 94–105). Association for Computational Linguistics.
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Muschalik, M., Fumagalli, F., Hammer, B., &; Huellermeier, E. (2024). Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence, 38(13), 14388–14396.https://doi.org/10.1609/aaai.v38i13.29352 
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DOI: 10.1609/aaai.v38i13.29352