Bild: Katrin Glückler
Forschungsinteressen
- Tree Ensemble Methods
- Deep Learning for Tabular Data
- Explainable Artificial Intelligence (XAI)
Lebenslauf
| Seit 2019 | Wissenschaftlicher Mitarbeiter am Institut für Enterprise Systems |
|---|---|
| 2017–2019 | Master of Science in Wirtschaftsinformatik (Data and Web Science) an der Universität Mannheim |
| 2014–2017 | Bachelor of Science in Wirtschaftsinformatik an der Universität Mannheim |
Publikationen
- Marton, S., Lüdtke, S., Bartelt, C., Tschalzev, A. und Stuckenschmidt, H. (2024). Explaining neural networks without access to training data. Machine Learning, 113, 3633-3652.
- 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. und 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. und Bartelt, C. (2022). Explanations for neural networks by neural networks. Applied Sciences, 12, 1–14.
- Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). GRANDE: Gradient-Based Decision Tree Ensembles for tabular data. In , International Conference on Learning Representations (S. 1–27). , OpenReview.net: .
- Marton, S., Lüdtke, S., Bartelt, C. und 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.
- Tschalzev, A., Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). A data-centric perspective on evaluating machine learning models for tabular data. In , The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (S. 1–35). , NeurIPS: Vancouver, BC.
- Ernst, J. S., Marton, S., Brinkmann, J., Vellasques, E., Foucard, D., Kraemer, M. und Lambert, M. (2023). Bias mitigation for large language models using adversarial learning. In , Proceedings of the 1st Workshop on Fairness and Bias in AI co-located with 26th European Conference on Artificial Intelligence (ECAI 2023),Kraków, Poland, October 1st, 2023 (S. 1–14). CEUR Workshop Proceedings, RWTH Aachen: Aachen, Germany.
- Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2023). GradTree: Learning axis-aligned decision trees with gradient descent. In , (S. 1–17). , Neural Information Processing Systems Foundation, Inc. (NeurIPS): New Orleans.
- Marton, S., Lüdtke, S., Bartelt, C. und Stuckenschmidt, H. (2024). GradTree: Learning axis-aligned decision trees with gradient descent. The 38th Annual AAAI Conference on Artificial Intelligence, Vancouver, Canada.
- Marton, S., Bartelt, C. und 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.