Publications

Publications


    Preprints

  • 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
  • 2024

  • 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), (to appear) 2024.
  • M. Nagda and S. Fellenz.
    Putting Back the Stops: Integrating Syntax with Neural Topic Models.
    Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), (to appear) 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), (to appear) 2024.
    https://arxiv.org/abs/2308.13577
  • 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
  • 2023

  • P. Ostheimer, M. Nagda, M. Kloft, and S. Fellenz.
    A Call for Standardization and Validation of Text Style Transfer Evaluation.
    Findings of the Association for Computational Linguistics (ACL), 10791-10815, 2023.
    https://arxiv.org/abs/2306.00539
  • A. Li, C. Qiu, P. Smyth, M. Kloft, S. Mandt, and M. Rudolph.
    Deep Anomaly Detection under Labeling Budget Constraints.
    Proceedings of the International Conference on Machine Learning (ICML), 19882-19910, 2023.
    https://openreview.net/forum?id=VjopP4ejwB
  • D. Wagner, T. Michels, F. Schulz, A. Nair, M. Rudolph, and M. Kloft.
    TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection.
    Transactions on Machine Learning Research (TMLR), 2023.
    https://ml.cs.uni-kl.de/publications/2023/TimeSeAD.pdf
  • 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.
    Chemie Ingenieur Technik, 95(7): 1077-1082, 2023.
    https://onlinelibrary.wiley.com/doi/10.1002/cite.202200238
  • Werner, J., Seidel, T., Jafar, R., Heese, R., Hasse, H., & Bortz, M.
    Multiplicities in thermodynamic activity coefficients.
    AIChE Journal, 69(12), e18251, 2023.