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, V. Fortuin.
On the Challenges and Opportunities in Generative AI.
arXiv:2403.00025, 2024. https://arxiv.org/abs/2403.00025➚
Jochen Schmid, Philipp Seufert, Michael Bortz.
Adaptive discretization algorithms for locally optimal experimental design.
arXiv:2406.01541, 2024. https://arxiv.org/abs/2406.01541➚
C. James, M. Nagda, N. Ghassemi, M. Kloft, and S. Fellenz.
Evaluating Dynamic Topic Models.
Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2024. https://aclanthology.org/2024.acl-long.11.pdf➚
M. Nagda and S. Fellenz.
Putting Back the Stops: Integrating Syntax with Neural Topic Models.
Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), 2024. https://www.ijcai.org/proceedings/2024/0710.pdf➚
S. Varshneya, A. Ledent, P. Liznerski, A. Balinskyy, P. Mehta, W. Mustafa, and M. Kloft.
Interpretable Tensor Fusion.
Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), (to appear) 2024. ➚
B. Franks, C. Morris, A. Velingker, and F. Geerts.
Weisfeiler-Leman at the margin: When more expressivity matters.
Proceedings of the International Conference on Machine Learning (ICML), (to appear) 2024. ➚
A. Muraleedharan, F. Hartung, D. Wagner, M. Kloft, and J. Burger.
Benchmarking Deep Anomaly Detection on Real Process Data of a Continuous Distillation Process.
ESCAPE34-PSE24 Symposium, (to appear) 2024. ➚
M. Nagda, P. Ostheimer, T. Specht, F. Rhein, F. Jirasek, M. Kloft, and S. Fellenz.
PITs: Physics-Informed Transformers for Predicting Chemical Phenomena.
Proceedings of the ECML PKDD Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), 2024. Full oral. https://ml4cce-ecml.com/papers/179.pdf➚
F. Hartung, B. Franks, D. Wagner, P. Liznerski, S. Reithermann, S. Fellenz, F. Jirasek, M. Rudolph, D. Neider, F. Rhein, H. Leitte, C. Song, B. Klöpper, S. Mandt, M. Bortz, J. Burger, H. Hasse, and M. Kloft.
Deep Anomaly Detection on Tennessee Eastman Procss Data.
Proceedings of the ECML PKDD Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), 2024. ➚
P. Ostheimer, M. Nagda, M. Kloft, and S. Fellenz.
Text Style Transfer Evaluation Using Large Language Models.
Proceedings of International Conference on Computational Linguistics (COLING), 15802-1582, 2024. https://aclanthology.org/2024.lrec-main.1373.pdf➚
M. Peter, N. Ghanooni, F. Hartung, B. Franks, D. Wagner, P. Liznerski, A. Muraleedharan, J. Arweiler, D. Reinhardt, I. Jungjohann, S. Reithermann, S. Fellenz, F. Jirasek, M. Rudolph, D. Neider, F. Rhein, H. Leitte, C. Song, S. Mandt, M. Bortz, J. Burger, H. Hasse, and M. Kloft.
Anomaly Classification of Tennessee-Eastman Process Data.
Proceedings of the ECML PKDD Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), 2024. ➚
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.
Proceedings of the ECML PKDD Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), 2024. https://ml4cce-ecml.com/papers/178.pdf➚
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.
Proceedings of the ECML PKDD Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), 2024. ➚
J. Will, J. Arweiler, I. Jungjohann, J. Werner, M. Nagda, M. Bortz, J. Schmid, M. Kloft, S. Fellenz, and S. Mandt.
Enhancing Realism in Batch Distillation Simulations: Data-Efficient Time Series Style Transfer with Transformers.
Proceedings of the ECML PKDD Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), 2024. https://ml4cce-ecml.com/papers/199.pdf➚
S. Lutz, J. Arweiler, A. Muraleedharan, N. Kahlhoff, J. Burger, M. Bortz, F. Hartung, H. Hasse, S. Fellenz, F. Jirasek, I. Jungjohann, M. Kloft, H. Leitte, S. Mandt, M. Nagda, D. Reinhardt, J. Schmid, D. Wagner, J. Werner, and D. Neider.
A Benchmark Suit for Neural Network Verification.
Proceedings of the ECML PKDD Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), 2024. ➚
W. Mustafa, P. Liznerski, A. Ledent, D. Wagner, P. Wang, and M. Kloft.
Non-vacuous PAC-Bayes bounds for Models under Adversarial Corruptions.
GAMM 2024. SPP 2298: Theoretical Foundations of Deep Learning, 2024. ➚
Philipp Liznerski, Saurabh Varshneya, Ece Calikus, Sophie Fellenz, Marius Kloft.
Reimagining Anomalies: What If Anomalies Were Normal?
arXiv:2402.14469, 2024. https://arxiv.org/abs/2402.14469➚
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt.
Zero-Shot Anomaly Detection via Batch Normalization.
arXiv:2302.07849v4, 2024. https://arxiv.org/abs/2302.07849➚
A. Li, C. Qiu, M. Kloft, P. Smyth, M. Rudolph, and S. Mandt.
Advances in Neural Information Processing Systems (NeurIPS) 36, 2023. Zero-Shot Batch-Level Anomaly Detection➚
A. Ledent, R. Alves, and M. Kloft.
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 34(5): 2259-2270, 2023. Orthogonal Inductive Matrix Completion➚
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.
Chemie Ingenieur Technik, 95(7): 1077-1082, 2023. Deep Anomaly Detection on Tennessee Eastman Process Data➚
