Researcher Portfolio
Prof. Dr. Haeberlen, Ulrich
Department of Molecular Physics, Max Planck Institute for Medical Research, Max Planck Society, Research Group Prof. Dr. Haeberlen, Max Planck Institute for Medical Research, Max Planck Society
Researcher Profile
Position: Research Group Prof. Dr. Haeberlen, Max Planck Institute for Medical Research, Max Planck Society
Position: Department of Molecular Physics, Max Planck Institute for Medical Research, Max Planck Society
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons93258
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Publications
(1 - 25 of 118)
: Walter, N. P., Vreeken, J., & Fischer, J. (2025). Now You See Me! A Framework for Obtaining Class-relevant Saliency Maps. Retrieved from https://arxiv.org/abs/2503.07346. [PubMan] : Walter, N. P., Fischer, J., & Vreeken, J. (2024). Finding Interpretable Class-Specific Patterns through Efficient Neural Search. In Proceedings of the 38th AAAI Conference on Artificial Intelligence (pp. 9062-9070). Palo Alto, CA: AAAI. doi:10.1609/aaai.v38i8.28756. [PubMan] : Kamp, M., Fischer, J., & Vreeken, J. (in press). Federated Learning from Small Datasets. In Eleventh International Conference on Learning Representations. OpenReview.net. [PubMan] : Fischer, J., Burkholz, R., & Vreeken, J. (2023). Preserving Local Densities in Low-dimensional Embeddings. Retrieved from https://arxiv.org/abs/2301.13732. [PubMan] : Hedderich, M. A., Fischer, J., Klakow, D., & Vreeken, J. (2023). Understanding and Mitigating Classification Errors Through Interpretable Token Patterns. Retrieved from https://arxiv.org/abs/2311.10920. [PubMan] : Coupette, C., Vreeken, J., & Rieck, B. (2023). All the world's a (hyper)graph: A data drama. Digital Scholarship in the Humanities, fqad071. doi:10.1093/llc/fqad071. [PubMan] : Hedderich, M. A., Fischer, J., Klakow, D., & Vreeken, J. (2022). Label-Descriptive Patterns and Their Application to Characterizing Classification Errors. In K. Chaudhuri, S. Jegelka, S. Le, S. Csaba, N. Gang, & S. Sabato (Eds. ), Proceedings of the 39th International Conference on Machine Learning (pp. 8691-8707). Retrieved from https://proceedings.mlr.press/v162/hedderich22a.html. [PubMan] : Coupette, C., Dalleiger, S., & Vreeken, J. (2022). Differentially Describing Groups of Graphs. Retrieved from https://arxiv.org/abs/2201.04064. [PubMan] : Coupette, C., Vreeken, J., & Rieck, B. (2022). All the World's a (Hyper)Graph: A Data Drama. Retrieved from https://arxiv.org/abs/2206.08225. [PubMan] : Coupette, C., Dalleiger, S., & Vreeken, J. (2022). Differentially Describing Groups of Graphs. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (pp. 3959-3967). Palo Alto, CA: AAAI. doi:10.1609/aaai.v36i4.20312. [PubMan] : Coupette, C., & Vreeken, J. (2021). Graph Similarity Description: How Are These Graphs Similar? In F. Zhu, B. C. Ooi, C. Miao, G. Cong, J. Tang, & T. Derr (Eds. ), KDD '21 (pp. 185-195). New York, NY: ACM. doi:10.1145/3447548.3467257. [PubMan] : Fischer, J., & Vreeken, J. (2021). Differentiable Pattern Set Mining. In F. Zhu, B. C. Ooi, C. Miao, G. Cong, J. Tang, & T. Derr (Eds. ), KDD '21 (pp. 383-392). New York, NY: ACM. doi:10.1145/3447548.3467348. [PubMan] : Hedderich, M., Fischer, J., Klakow, D., & Vreeken, J. (2021). Label-Descriptive Patterns and their Application to Characterizing Classification Errors. Retrieved from https://arxiv.org/abs/2110.09599. [PubMan] : Heiter, E., Fischer, J., & Vreeken, J. (2021). Factoring Out Prior Knowledge from Low-dimensional Embeddings. Retrieved from https://arxiv.org/abs/2103.01828. [PubMan] : Budhathoki, K., Boley, M., & Vreeken, J. (2021). Discovering Reliable Causal Rules. In C. Demeniconi, & I. Davidson (Eds. ), Proceedings of the SIAM International Conference on Data Mining (pp. 1-9). Philadelphis, PA: SIAM. doi:10.1137/1.9781611976700.1. [PubMan] : Fischer, J., Oláh, A., & Vreeken, J. (2021). What’s in the Box? Exploring the Inner Life of Neural Networks with Robust Rules. In M. Meila, & T. Zhang (Eds. ), Proceedings of the 38th International Conference on Machine Learning (pp. 3352-3362). MLR Press. [PubMan] : Mian, O. A., Marx, A., & Vreeken, J. (2021). Discovering Fully Oriented Causal Networks. In Thirty-Fifth AAAI Conference on Artificial Intelligence (pp. 8975-8982). Palo Alto, CA: AAAI. doi:10.1609/aaai.v35i10.17085. [PubMan] : Kalofolias, J., Welke, P., & Vreeken, J. (2021). SUSAN: The Structural Similarity Random Walk Kernel. In C. Demeniconi, & I. Davidson (Eds. ), Proceedings of the SIAM International Conference on Data Mining (pp. 298-306). Philadelphis, PA: SIAM. doi:10.1137/1.9781611976700.34. [PubMan] : Schmidt, F., Marx, A., Baumgarten, N., Hebel, M., Wegner, M., Kaulich, M., Leisegang, M. S., Brandes, R. P., Göke, J., Vreeken, J., & Schulz, M. H. (2021). Integrative Analysis of Epigenetics Data Identifies Gene-specific Regulatory Elements. Nucleic Acids Research (London), 49(18), 10397-10418. doi:10.1093/nar/gkab798. [PubMan] : Kamp, M., Fischer, J., & Vreeken, J. (2021). Federated Learning from Small Datasets. Retrieved from https://arxiv.org/abs/2110.03469. [PubMan] : Belth, C., Zheng, X., Vreeken, J., & Koutra, D. (2020). What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization. In Y. Huang, I. King, T.-Y. Liu, & M. van Steen (Eds. ), Proceedings of The World Wide Web Conference (pp. 1115-1126). New York, NY: ACM. doi:10.1145/3366423.3380189. [PubMan] : Mandros, P., Boley, M., & Vreeken, J. (2020). Discovering Dependencies with Reliable Mutual Information. Knowledge and Information Systems, 62, 4223-4253. doi:10.1007/s10115-020-01494-9. [PubMan] : Dalleiger, S., & Vreeken, J. (2020). Explainable Data Decompositions. In AAAI Technical Track: Machine Learning (pp. 3709-3716). Palo Alto, CA: AAAI. doi:10.1609/aaai.v34i04.5780. [PubMan] : Dalleiger, S., & Vreeken, J. (2020). The Relaxed Maximum Entropy Distribution and its Application to Pattern Discovery. In C. Plant, H. Wang, A. Cuzzocrea, C. Zaniolo, & X. Wu (Eds. ), 20th IEEE International Conference on Data Mining (pp. 978-983). Piscataway, NJ: IEEE. doi:10.1109/ICDM50108.2020.00112. [PubMan] : Sutton, C., Boley, M., Ghiringhelli, L., Rupp, M., Vreeken, J., & Scheffler, M. (2020). Identifying Domains of Applicability of Machine Learning Models for Materials Science. Nature Communications, 11: 4428. doi:10.1038/s41467-020-17112-9. [PubMan]