Research Interests
- Explainability of Machine Learning Models
- Automated Machine Learning (AutoML)
- Time Series Forecasting
Short CV
| Since 2019 | Research Assistant at the Institute for Enterprise Systems |
|---|---|
| 2017–2019 | Master of Science in Business Informatics (Data and Web Science) at the University of Mannheim |
| 2014–2017 | Bachelor of Science in Business Informatics at the University of Mannheim |
Publications
- Marton, S., Lüdtke, S., Bartelt, C., Tschalzev, A. and Stuckenschmidt, H. (2024). Explaining neural networks without access to training data. Machine Learning, 1–20.
- Umlauft, J., Johnson, C. W., Roux, P., Trugman, D. T., Lecointre, A., Walpersdorf, A., Nanni, U., Gimbert, F., Rouet-Leduc, B., Hulbert, C., Lüdtke, S., Marton, S. and Johnson, P. A. (2023). Mapping glacier basal sliding applying machine learning. Journal of geophysical research : JGR. F, Earth surface, 128, 1–20.
- Marton, S., Lüdtke, S. and Bartelt, C. (2022). Explanations for neural networks by neural networks. Applied Sciences, 12, 1–14.
- Marton, S., Lüdtke, S., Bartelt, C. and Stuckenschmidt, H. (2024). GradTree: Learning axis-aligned decision trees with gradient descent. In , Proceedings of the 38th AAAI Conference on Artificial Intelligence (S. 14323-14331). , AAAI Press: Washington, DC.
- Marton, S., Bartelt, C. and Stuckenschmidt, H. (2020). Machine learning for converting Black-Box models to interpretable functions. PhD Forum, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020, Online.