Publications

Publications


2025  2024  2023  2022

Preprints

  • D. Wagner, A. Nair, B. J. Franks, J. Arweiler, A. Muraleedharan, I. Jungjohann, F. Hartung, M. C. Ahuja, A. Balinskyy, N. H. Syed, M. Nagda, P. Liznerski, S. Reithermann, M. Rudolph, S. Vollmer, R. Schulz, T. Katz, S. Mandt, M. Bortz, H. Leitte, D. Neider, J. Burger, F. Jirasek, H. Hasse, S. Fellenz, and M. Kloft. “Formally exploring time-series anomaly detection evaluation metrics”. In: AISTATS. Vol. abs/2510.17562. 2026. doi: 10.48550/arXiv.2510.17562. url: https://arxiv.org/abs/2510.17562.
  • F. A. Zaid, D. Neider, and M. Yalçiner. “VeriFlow: Modeling distributions for neural network verification”. In: AAAI. AAAI Press, 2026, to appear. doi: 10.48550/ARXIV.2406.14265. arXiv: 2406.14265. url: https://doi.org/10.48550/arXiv.2406.14265.
  • M. Nagda, J. Abijuru, P. Ostheimer, M. Kloft, and S. Fellenz. PIANO: Physics informed autoregressive network. 2026. doi: 10.48550/arXiv.2508.16235. arXiv: 2508.16235 (cs.LG). url: https://arxiv.org/abs/2508.16235. Submitted.
  • M. Nagda, P. Ostheimer, T. Specht, F. Rhein, F. Jirasek, S. Mandt, M. Kloft, and S. Fellenz. “SetPINNs: Set-based physics-informed neural networks”. In: AISTATS. Spotlight (2.5% of papers). 2026. doi: 10.48550/arXiv.2409.20206. url: https://arxiv.org/abs/2409.20206.
  • P. Liznerski, S. Varshneya, E. Calikus, P. Wang, A. Bartscher, S. J. Vollmer, S. Fellenz, and M. Kloft. “Reimagining anomalies: What if anomalies were normal?” In: AAAI. 2026. doi: 10.48550/arXiv.2402.14469. url: https://arxiv.org/abs/2402.14469.
  • J. Werner, I. Jungjohann, J. Arweiler, J. Schmid, H. Hasse, F. Jirasek, and M. Bortz. “Simulation data to augment experimental data for anomaly detection in batch distillation”. Submitted version will be available for inspection in April. 2026.
  • W. Li, W. Mustafa, M. Monteiro, P. Wang, M. Kloft, and S. Fellenz. “TORA: Train once, realign anytime for offline multi-objective reinforcement learning”. In: AAAI. (to appear). 2026.

2025

  • A. Muraleedharan, A. Ferre, J. Arweiler, I. Jungjohann, F. Jirasek, H. Hasse, and J. Burger. “Experimental time series data with and without anomalies from a continuous distillation mini-plant for development of machine learning anomaly detection methods”. In: engrXiv:5631 (2025). doi: 10.31224/5631. url: https://engrxiv.org/preprint/view/5631.
  • C. Qiu, M. Kloft, S. Mandt, and M. Rudolph. “Self-supervised anomaly detection with neural transformations”. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2025). doi: 10.1109/tpami.2024.3519543. url: https://pubmed.ncbi.nlm.nih.gov/40030646/.
  • 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”. In: NeurIPS. 2025. url: https://neurips.cc/virtual/2025/loc/san-diego/poster/121442.
  • J. Abijuru, M. Nagda, P. Ostheimer, J. C. Aurich, S. J. Vollmer, M. Kloft, and S. Fellenz. “Physics-informed residual flows”. In: DiffSys Workshop. 2025. url: https://openreview. net/forum?id=OiIoXAUGHZ.
  • J. Arweiler, I. Jungjohann, A. Muraleedharan, H. Leitte, J. Burger, K. Münnemann, F. Jirasek, and H. Hasse. “Batch distillation data for developing machine learning anomaly detection methods”. In: arXiv:2510.18075 (2025). doi: 10.48550/arXiv.2510.18075. url: https://arxiv.org/abs/2510.18075.
  • J. Arweiler, I. Jungjohann, A. Muraleedharan, H. Leitte, J. Burger, K. Münnemann, F. Jirasek, and H. Hasse. “Batch distillation data for developing machine learning anomaly detection methods”. In: Zenodo (2025). doi: 10.5281/zenodo.17395543. url: https://doi.org/10.5281/zenodo.17395543.
  • J. Tauberschmidt, S. Fellenz, S. J. Vollmer, and A. B. Duncan. “Physics-constrained finetuning of flow-matching models for generation and inverse problems”. In: ICLR. OpenReview submission. 2025. url: https://openreview.net/forum?id=khBHJz2wcV. accepted.
  • J. Werner, J. Schmid, L. T. Biegler, and M. Bortz. “An equation-based batch distillation simulation to evaluate the effect of multiplicities in thermodynamic activity coefficients”. In: Fluid Phase Equilibria 598 (2025), p. 114465. doi: 10.1016/j.fluid.2025.114465. url: https://www.sciencedirect.com/science/article/pii/S0378381225001359.
  • J. Werner, T. Seidel, H. Hasse, and M. Bortz. “Removing mutliplicities from activity coefficient models”. In: arXive (2025). doi: 10.1002/aic.18251. url: https://aiche.onlinelibrary.wiley.com/doi/10.1002/aic.18251.
  • L. Vollmer, R. Loubet, F. Jirasek, S. Fellenz, H. Hasse, and H. Leitte. “Enabling transparent problem solving in thermodynamics with ontologies and knowledge graphs”. In: ESWC 2025 Workshops and Tutorials Joint Proceedings (ESWC-JP 2025). 2025. url: https://ceur-ws.org/Vol-3977/XAIKG-3.pdf.
  • L. Westhofen, J. C. Jung, and D. Neider. “Temporal conjunctive query answering via rewriting”. In: AAAI. Ed. by T. Walsh, J. Shah, and Z. Kolter. AAAI Press, 2025, pp. 15221–15229. doi: 10.1609/AAAI.V39I14.33670. url: https://doi.org/10.1609/aaai.v39i14.33670.
  • M. Hoffmann, H. Hasse, and F. Jirasek. “GRAPPA—A hybrid graph neural network for predicting pure component vapor pressures”. In: Chemical Engineering Journal Advances (2025), p. 100750. doi: 10.1016/j.ceja.2025.100750. url: https://arxiv.org/abs/2501.08729.
  • M. Hoffmann, T. Specht, Q. Göttl, J. Burger, S. Mandt, H. Hasse, and F. Jirasek. “A machinelearned expression for the excess Gibbs energy”. In: arXiv:2509.06484 (2025). doi: 10.48550/arXiv.2509.06484. url: https://arxiv.org/abs/2509.06484.
  • M. Nagda, J. Abijuru, P. Ostheimer, J. C. Aurich, S. Mandt, M. Kloft, and S. Fellenz. “Autoregressive PINNs for time-dependent PDEs”. In: DiffSys Workshop. 2025. url: https://openreview.net/forum?id=1iOZVPmudk.
  • M. Nagda, P. Ostheimer, and S. Fellenz. “Tethering broken themes: Aligning neural topic models with labels and authors”. In: Findings of the Association for Computational Linguistics: NAACL 2025, Albuquerque, New Mexico, USA, April 29 – May 4, 2025. Ed. by L. Chiruzzo, A. Ritter, and L. Wang. Association for Computational Linguistics, 2025, pp. 740–760. doi: 10.18653/V1/2025.FINDINGS- NAACL.44. url: https://doi.org/10.18653/v1/2025.findings-naacl.44.
  • M. Nagda, P. Ostheimer, J. Arweiler, I. Jungjoha, 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”. In: ECML Workshop SynDAiTE. 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. DiffStyleTS: Diffusion model for style transfer in time series. 2025. doi: 10.48550/arXiv.2510.11335. arXiv: 2510.11335 (cs.LG). url: https://arxiv.org/abs/2510.11335.
  • N. Ghanooni, W. Mustafa, D. Wagner, S. Fellenz, A. W. Lin, and M. Kloft. “Mitigating spurious features in contrastive learning with spectral regularization”. In: NeurIPS. 2025. url: https://neurips.cc/virtual/2025/loc/san-diego/poster/117854.
  • N. Hayer, T. Wendel, S. Mandt, H. Hasse, and F. Jirasek. “Advancing thermodynamic groupcontribution methods by machine learning: UNIFAC 2.0”. In: Chemical Engineering Journal 504 (2025), p. 158667. doi: 10.1016/j.cej.2024.158667. url: https://www.sciencedirect.com/science/article/pii/S1385894724101581.
  • 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”. In: EurIPS 2025 Workshop on SIMBIOCHEM. 2025. url: https://openreview.net/forum?id=O3OVn3NSRE.
  • R. Loubet, P. Zittlau, M. Hoffmann, L. Vollmer, S. Fellenz, H. Leitte, F. Jirasek, J. Lenhard, and H. Hasse. Superstudent intelligence in thermodynamics. 2025. doi: 10.48550/arXiv.2506.09822. arXiv: 2506.09822 (cs.CE). url: https://arxiv.org/abs/2506.09822.
  • S. Lutz, D. Kaminskyi, F. Wittbold, S. Dierl, F. Howar, B. König, E. Müller, and D. Neider. “Unsupervised automata learning via discrete optimization”. In: JELIA. Ed. by G. Casini, B. Dundua, and T. Kutsia. Vol. 16093. Lecture Notes in Computer Science. Springer, 2025, pp. 135–153. doi: 10.1007/978-3-032-04587-4\_9. url: https://doi.org/10.1007/9783-032-04587-4%5C_9.
  • V. Belle, M. Benedikt, D. Drachsler-Cohen, D. Neider, and T. Yuviler. “Logic and neural networks (Dagstuhl seminar 25061)”. In: Dagstuhl Reports 15.2 (2025), pp. 1–20. doi: 10.4230/DAGREP.15.2.1. url: https://doi.org/10.4230/DagRep.15.2.1.

2024

  • A. Ferre, J. Voggenreiter, C. F. Breitkreuz, D. Worch, U. Lubenau, H. Hasse, and J. Burger. “Experimental demonstration of the production of poly (oxymethylene) dimethyl ethers from methanolic formaldehyde solutions in a closed-loop mini-plant”. In: Chemical Engineering Research and Design 211.28 (2024), pp. 331–342. doi: 10.1016/j.cherd.2024.09.041. url: https://doi.org/10.1016/j.cherd.2024.09.041.
  • A. Muraleedharan, F. Hartung, D. Wagner, M. Kloft, and J. Burger. “Benchmarking deepanomaly detection on real process data of a continuous distillation process”. In: ESCAPE34PSE24. 2024. url: https://www.aidic.it/escape34-pse24//programma/138Muraleedharan.docx.
  • B. Franks, C. Morris, A. Velingker, and F. Geerts. Weisfeiler-Leman at the margin: When more expressivity matters. ICML, 2024.
  • C. James, M. Nagda, N. Ghassemi, M. Kloft, and S. Fellenz. “Evaluating Dynamic Topic Models”. In: Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL) (2024). url: https://aclanthology.org/2024.acl-long.11.pdf.
  • C. Karakkaparambil James, M. Nagda, N. Haji Ghassemi, M. Kloft, and S. Fellenz. “Evaluating Dynamic Topic Models”. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Bangkok, Thailand: Association for Computational Linguistics, Aug. 2024, pp. 160–176. doi: 10.18653/v1/2024.acl-long.11. url: https://aclanthology.org/2024.acl-long.11/.
  • D. Hintersdorf, L. Struppek, D. Neider, and K. Kersting. “Defending our privacy with backdoors”. In: ECAI. Ed. by U. Endriss, F. S. Melo, K. Bach, A. J. B. Diz, J. M. Alonso-Moral, S. Barro, and F. Heintz. Vol. 392. Frontiers in Artificial Intelligence and Applications. IOS Press, 2024, pp. 1832–1839. doi: 10.3233/FAIA240695. url: https://doi.org/10.3233/FAIA240695.
  • D. Neider and R. Roy. “What is formal verification without specifications? A survey on mining LTL specifications”. In: Principles of Verification: Cycling the Probabilistic Landscape – Essays Dedicated to Joost-Pieter Katoen on the Occasion of His 60th Birthday, Part III. Ed. by N. Jansen, S. Junges, B. L. Kaminski, C. Matheja, T. Noll, T. Quatmann, M. Stoelinga, and M. Volk. Vol. 15262. Lecture Notes in Computer Science. Springer, 2024, pp. 109–125. doi: 10.1007/978-3-031-75778-5\_6. url: https://doi.org/10.1007/978-3-031-757785%5C_6.
  • D. Reinhardt, D. Wagner, A. Muraleedharan, J. Arweiler, I. Jungjohann, F. Jirasek, J. Burger, H. Hasse, M. Kloft, and H. Leitte. “cPAX: Comparative visualization of known and novel anomalies for monitoring chemical plants”. In: ECML PKDD Workshop ML4CCE. 2024. url: https://ml4cce-ecml.com/papers/178.pdf.
  • F. Hartung et al. Deep Anomaly Detection on Tennessee Eastman Process Data. ML4CCE @ ECML PKDD, 2024.
  • J. Arweiler, A. Muraleedharan, F. Hartung, I. Jungjohann, D. Wagner, M. Kloft, J. Burger, H. Hasse, and F. Jirasek. “Anomaly detection on experimental chemical process data”. In: ECML PKDD Workshop ML4CCE. 2024. url: https://ml4cce-ecml.com/papers/184.pdf.
  • J. Schmid, P. Seufert, and M. Bortz. Adaptive discretization algorithms for locally optimal experimental design. arXiv:2406.01541, 2024. url: https://arxiv.org/abs/2406.01541.
  • J. Will, J. Arweiler, I. Jungjohann, J. Werner, M. Nagda, M. Bortz, H. Hasse, F. Jirasek, J. Schmid, M. Kloft, S. Mandt, and S. Fellenz. “Enhancing realism in batch distillation simulations: Data-efficient time series style transfer with transformers”. In: ECML PKDD Workshop ML4CCE. 2024. url: https://ml4cce-ecml.com/papers/199.pdf.
  • 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. d. Melo, E. Nalisnick, B. Ommer, R. Ranganath, M. Rudolph, K. Ullrich, G. v. d. Broeck, J. Vogt, Y. Wang, F. Wenzel, F. Wood, S. Mandt, and V. Fortuin. “On the challenges and opportunities in generative AI”. In: TMLR abs/2403.00025 (2024). doi: 10.48550/arXiv.2403.00025. url: https://arxiv.org/abs/2403.00025.
  • L. Streib, J. Spaak, M. Kloft, and R. Schäfer. “The spatiotemporal profile and adaptation determine the joint effects and interactions of multiple stressors”. In: Environmental Sciences Europe 36 (2024), p. 118.
  • L. Streib, J. W. Spaak, M. Kloft, and R. B. Schaefer. “The spatiotemporal profile and adaptation determine the joint effects and interactions of multiple stressors”. In: Environmental Sciences Europe 36.1 (2024), p. 118. doi: 10.1186/s12302-024-00945-2. url: https://doi.org/10.1186/s12302-024-00945-2.
  • L. Vollmer, S. Fellenz, F. Jirasek, H. Hasse, and H. Leitte. “KnowTD – An actionable knowledge representation system for thermodynamics”. In: Journal of Chemical Information and Modeling 64.15 (2024), pp. 5878–5887. doi: 10.1021/acs.jcim.4c00647. url: https://arxiv.org/abs/2407.17169.
  • M. Hussong, S. Varshneya, P. Ruediger-Flore, M. Glatt, M. Kloft, and J. C. Aurich. “A process planning system using deep artificial neural networks for the prediction of operation sequences”. In: Procedia CIRP 120 (2024), pp. 135–140. doi: 10.1016/j.procir.2023.08.025. url: https://doi.org/10.1016/j.procir.2023.08.025.
  • M. Hussong, S. Varshneya, P. Rüdiger-Flore, M. Glatt, M. Kloft, and J. C. Aurich. “A process planning system using deep artificial neural networks for the prediction of operation sequences”. In: Procedia CIRP 120 (2024), pp. 135–140.
  • M. Mohler. “Simulation of trajectories of individual cs atoms in dissipative and conservative force fields, and their analysis with the support of artificial intelligence”. Advisors: Prof. Dr. Artur Widera; Prof. Dr. Marius Kloft. Master’s Thesis. Kaiserslautern, Germany: RPTU University of Kaiserslautern-Landau, 2024. url: https://physik.rptu.de/fileadmin/widera/public_files/theses/Diplomarbeit_Marco_Mohler.pdf.
  • M. Nagda and S. Fellenz. “Putting back the stops: Integrating syntax with neural topic models”. In: IJCAI. ijcai.org, 2024, pp. 6424–6432. doi: 10.24963/ijcai.2024/710. url: https://www.ijcai.org/proceedings/2024/710.
  • M. Nagda, P. Ostheimer, T. Specht, F. Rhein, F. Jirasek, M. Kloft, and S. Fellenz. “PITs: Physics-informed transformers for predicting chemical phenomena”. In: ECML PKDD, Workshop ML4CCE. 2024. url: https://ml4cce-ecml.com/papers/179.pdf.
  • M. Peter et al. Anomaly Classification of Tennessee-Eastman Process Data. ML4CCE @ ECML PKDD, 2024.
  • P. Ostheimer, M. Nagda, M. Kloft, and S. Fellenz. Text Style Transfer Evaluation Using Large Language Models. COLING, 2024.
  • R. Alves, A. Ledent, R. Assuncao, P. Vaz-de-Melo, and M. Kloft. “Unraveling the Dynamics of Stable and Curious Audiences in Web Systems”. In: Proceedings of the ACM Web Conference 2024. ACM, 2024, pp. 2464–2475. doi: 10.1145/3589334.3645473. url: https://doi.org/10.1145/3589334.3645473.
  • R. Alves, A. Ledent, R. Assuncao, P. Vaz-de-Melo, and M. Kloft. “Unraveling the Dynamics of Stable and Curious Audiences in Web Systems”. In: Proceedings of The Web Conference (WWW) (2024), pp. 2464–2475.
  • R. Lopes, R. Alves, A. Ledent, R. L. T. Santos, and M. Kloft. “Recommendations with minimum exposure guarantees: a post-processing framework”. In: Expert Systems with Applications 236 (2024), p. 121164. doi: 10.1016/j.eswa.2023.121164. url: https://doi.org/10.1016/j.eswa.2023.121164.
  • R. Lopes, R. Alves, A. Ledent, R. Santos, and M. Kloft. “Recommendations with minimum exposure guarantees: A post-processing framework”. In: Expert Systems with Applications 236 (2024), p. 121164.
  • S. Lutz and D. Neider. “Interpretable machine learning via linear temporal logic”. en. In: sAIOnARA (2024). doi: 10.11576/DATANINJA-1176. url: https://biecoll.ub.uni-bielefeld.de/index.php/dataninja/article/view/1176.
  • S. Lutz et al. A Benchmark Suit for Neural Network Verification. ML4CCE @ ECML PKDD, 2024.
  • S. Lutz, J. Arweiler, A. Muraleedharan, N. Kahlhoff, F. Hartung, I. Jungjohann, M. Nagda, D. Reinhardt, D. Wagner, J. Werner, J. Will, J. Burger, M. Bortz, H. Hasse, S. Fellenz, F. Jirasek, M. Kloft, H. Leitte, S. Mandt, S. Reithermann, J. Schmid, and D. Neider. “A Benchmark suite for neural network verification”. In: ECML PKDD Workshop ML4CCE. 2024.
  • S. Lutz, J. Arweiler, A. Muraleedharan, N. Kahlhoff, F. Hartung, I. Jungjohann, M. Nagda, D. Reinhardt, D. Wagner, J. Werner, J. Will, J. Burger, M. Bortz, H. Hasse, S. Fellenz, F. Jirasek, M. Kloft, H. Leitte, S. Mandt, S. Reithermann, J. Schmid, and D. Neider. “A benchmark suite for verifying neural anomaly detectors in distillation processes”. In: ECML PKDD. 2024, to appear. url: https://ml4cce-ecml.com/papers/205.pdf.
  • S. Varshneya, A. Ledent, P. Liznerski, A. Balinskyy, P. Mehta, W. Mustafa, and M. Kloft. “Interpretable Tensor Fusion”. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (2024).
  • S. Varshneya, A. Ledent, P. Liznerski, A. Balinskyy, P. Mehta, W. Mustafa, and M. Kloft. “Interpretable Tensor Fusion”. In: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24. Ed. by K. Larson. Main Track. International Joint Conferences on Artificial Intelligence Organization, Aug. 2024, pp. 5037–5045. doi: 10.24963/ijcai.2024/557. url: https://doi.org/10.24963/ijcai.2024/557.
  • T. Katzke, S. Lutz, E. Müller, and D. Neider. “Provable guarantees for deep learning-based anomaly detection through logical constraints”. en. In: sAIOnARA (2024). doi: 10.11576/DATANINJA-1174. url: https://biecoll.ub.uni-bielefeld.de/index.php/dataninja/article/view/1174.
  • T. Specht, M. Nagda, S. Fellenz, S. Mandt, H. Hasse, and F. Jirasek. “HANNA: Hardconstraint neural network for consistent activity coefficient prediction”. In: Chemical Science 15.47 (2024), pp. 19777–19786. doi: 10.1039/d4sc05115g. url: https://arxiv.org/abs/2407.18011.
  • W. Li, R. Devidze, W. Mustafa, and S. Fellenz. “Ethics in Action: Training Reinforcement Learning Agent for Moral Decision-making In Text-based Adventure Games”. In: International Conference on Artificial Intelligence and Statistics (AISTATS). Proceedings of Machine Learning Research 238 (2024), pp. 1954–1962.
  • W. Li, R. Devidze, W. Mustafa, and S. Fellenz. “Ethics in Action: Training Reinforcement Learning Agents for Moral Decision-making In Text-based Adventure Games”. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Ed. by S. Dasgupta, S. Mandt, and Y. Li. Vol. 238. Proceedings of Machine Learning Research. PMLR, Feb. 2024, pp. 1954–1962. url: https://proceedings.mlr.press/v238/li24i.html.
  • W. Mustafa, P. Liznerski, A. Ledent, D. Wagner, P. Wang, and M. Kloft. “Non-vacuous Generalization Bounds for Adversarial Risk in Stochastic Neural Networks”. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Ed. by S. Dasgupta, S. Mandt, and Y. Li. Vol. 238. Proceedings of Machine Learning Research. PMLR, Feb. 2024, pp. 4528–4536. url: https://proceedings.mlr.press/v238/mustafa24a.html.
  • W. Mustafa, P. Liznerski, A. Ledent, D. Wagner, P. Wang, and M. Kloft. “Non-vacuous PAC-Bayes bounds for Models under Adversarial Corruptions”. In: GAMM 2024. SPP 2298: Theoretical Foundations of Deep Learning (2024).
  • W. Mustafa, P. Liznerski, A. Ledent, D. Wagner, P. Wang, and M. Kloft. “Numerically Tight Generalization Bounds for Adversarial Risk in Stochastic Neural Networks”. In: International Conference on Artificial Intelligence and Statistics (AISTATS). Proceedings of Machine Learning Research 238 (2024), pp. 4528–4536.

2023

  • A. Bhat et al. Constraint-Based Parameterization and Disentanglement of Aerodynamic Shapes using Deep Generative Models. ECML PKDD, 2023.
  • A. Ledent, R. Alves, and M. Kloft. Orthogonal Inductive Matrix Completion. TNNLS, 2023.
  • A. Ledent, R. Alves, Y. Lei, Y. Guermeur, and M. Kloft. “Generalization Bounds for Inductive Matrix Completion in Low-noise Settings”. In: Proceedings of the AAAI Conference on Artificial Intelligence (2023).
  • A. Li et al. Zero-Shot Batch-Level Anomaly Detection. NeurIPS, 2023.
  • A. Li, C. Qiu, M. Kloft, P. Smyth, M. Rudolph, and S. Mandt. “Zero-shot anomaly detection via batch normalization”. In: NeurIPS 36 (2023), pp. 40963–40993. url: https://proceedings.neurips.cc/paper_files/paper/2023file/8078e8c3055303a884ffae2d3ea00338Paper-Conference.pdf.
  • A. Li, C. Qiu, M. Kloft, P. Smyth, S. Mandt, and M. Rudolph. “Deep anomaly detection under labeling budget constraints”. In: ICML. JMLR.org, 2023. url: https://proceedings.mlr.press/v202/li23x.html.
  • A. Mukherjee, M. Glatt, W. Mustafa, M. Kloft, and J. Aurich. “Designing Resilient Manufacturing Systems using Cross Domain Application of Machine Learning Resilience”. In: Procedia CIRP 115 (2023), pp. 83–88.
  • A. Murano, D. Neider, and M. Zimmermann. “Robust alternating-time temporal logic”. In: JELIA. Ed. by S. A. Gaggl, M. V. Martinez, and M. Ortiz. Vol. 14281. Lecture Notes in Computer Science. Springer, 2023, pp. 796–813. doi: 10.1007/978-3-031-43619-2\_54. url: https://doi.org/10.1007/978-3-031-43619-2%5C_54.
  • B. Franks et al. A Systematic Approach to Universal Random Features in Graph Neural Networks. TMLR, 2023.
  • B. Romshoo et al. Machine learning algorithm for predicting black carbon (BC) optical properties at various stages of aging. EAC, 2023.
  • D. Neider and T. T. Johnson. “Track C1: Safety verification of deep neural networks (DNNs)”. In: AISoLA. Ed. by B. Steffen. Vol. 14380. Lecture Notes in Computer Science. Springer, 2023, pp. 217–224. doi: 10.1007/978-3-031-46002-9\_12. url: https://doi.org/10.1007/9783-031-46002-9%5C_12.
  • D. Wagner, T. Michels, F. C. Schulz, A. Nair, M. Rudolph, and M. Kloft. “TimeSeAD: Benchmarking deep multivariate time-series anomaly detection”. In: TMLR 2023 (2023). url: https://openreview.net/forum?id=iMmsCI0JsS.
  • F. Hartung, B. J. Franks, T. Michels, D. Wagner, P. Liznerski, S. Reithermann, S. Fellenz, F. Jirasek, M. Rudolph, D. Neider, H. Leitte, C. Song, B. Kloepper, S. Mandt, M. Bortz, J. Burger, H. Hasse, and M. Kloft. “Deep anomaly detection on Tennessee Eastman process data”. In: Chemie Ingenieur Technik 95.7 (2023), pp. 1077–1082. doi: 10.1002/cite.202200238. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/cite.202200238. url: https://onlinelibrary.wiley.com/doi/abs/10.1002/cite.202200238.
  • F. Jirasek et al. Making thermodynamic models predictive by machine learning: matrix completion of pair interactions. ECML/PKDD Workshop, 2023.
  • I. Khmelnitsky, D. Neider, R. Roy, X. Xie, B. Barbot, B. Bollig, A. Finkel, S. Haddad, M. Leucker, and L. Ye. “Analysis of recurrent neural networks via property-directed verification of surrogate models”. In: Int. J. Softw. Tools Technol. Transf. 25.3 (2023), pp. 341–354. doi: 10.1007/S10009-022-00684-W. url: https://doi.org/10.1007/s10009-022-00684-w.
  • J. Werner, T. Seidel, R. Jafar, R. Heese, H. Hasse, and M. Bortz. “Multiplicities in thermodynamic activity coefficients”. In: AIChE Journal 69.12 (2023), e18251. doi: 10.1002/aic.18251. eprint: https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.18251. url: https://aiche.onlinelibrary.wiley.com/doi/abs/10.1002/aic.18251.
  • K. Bykov et al. Labeling Neural Representations with Inverse Recognition. NeurIPS, 2023.
  • M. Kirchler, C. Lippert, and M. Kloft. “Training normalizing flows from dependent data”. In: ICML. Ed. by A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett. Vol. 202. PMLR. PMLR, July 2023, pp. 17105–17121. url: https://proceedings.mlr.press/v202/kirchler23a.html.
  • P. Ostheimer, M. Nagda, M. Kloft, and S. Fellenz. A Call for Standardization and Validation of Text Style Transfer Evaluation. ACL Findings, 2023.
  • S. Lutz, D. Neider, and R. Roy. “Specification sketching for linear temporal logic”. In: ATVA. Ed. by É. André and J. Sun. Vol. 14216. Lecture Notes in Computer Science. Springer, 2023, pp. 26–48. doi: 10.1007/978-3-031-45332-8\_2. url: https://doi.org/10.1007/978-3031-45332-8%5C_2.
  • W. Li, R. Devidze, and S. Fellenz. “Learning to play text-based adventure games with maximum entropy reinforcement learning”. In: Machine Learning and Knowledge Discovery in Databases (ECML PKDD) (2023), pp. 39–54.
  • W. Mustafa, P. Liznerski, D. Wagner, P. Wang, and M. Kloft. “Computing non-vacuous PAC-Bayes generalization bounds for Models under Adversarial Corruptions”. In: ICML 2023 Workshop on PAC-Bayes Meets Interactive Learning (2023).

2022

  • F. Jirasek, R. Bamler, S. Fellenz, M. Bortz, M. Kloft, S. Mandt, and H. Hasse. Making Thermodynamic Models of Mixtures Predictive by Machine Learning: Matrix Completion of Pair Interactions. Chemical Science 13, 4854-4862, 2022.
  • A. Chawda, M. Kloft, and S. Grimm. “Unsupervised Anomaly Detection for Auditing Data and Impact of Categorial Encodings”. In: NeurIPS 2022 Workshop on SyntheticData4ML (2022).
  • P. Liznerski, L. Ruff, R. A. Vandermeulen, B. J. Franks, K.-R. Müller, and M. Kloft. “Exposing outlier exposure: What can be learned from few, one, and zero outlier images”. In: TMLR (2022). url: https://openreview.net/forum?id=3v78awEzyB.