Researcher Portfolio
Gaquerel, Emmanuel
Department of Molecular Ecology, Prof. I. T. Baldwin, MPI for Chemical Ecology, Max Planck Society
Researcher Profile
Position: Department of Molecular Ecology, Prof. I. T. Baldwin, MPI for Chemical Ecology, Max Planck Society
Additional IDs: IRIS: 3480
Researcher ID: https://pure.mpg.de/cone/persons/resource/persons3878
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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]