Boobier, S., Heeley, J., Gärtner, T., & Hierst, J. D. (2025). Interactive Knowledge-Based Kernel PCA for Solvent Selection. ACS Sustainable Chemistry & Engineering, 3(11), 4349–4368. https://doi.org/10.1021/acssuschemeng.4c07974 ( reposiTUm)

Phan, T.-L., Klaus Weinbauer, Thomas Gärtner, Merkle, D., Andersen, J., Fagerberg, R., & Stadler, P. F. (2024). Reaction rebalancing: a novel approach to curating reaction databases. Journal of Cheminformatics, 16(1), Article 82. https://doi.org/10.1186/s13321-024-00875-4 ( reposiTUm)

Alkhoury, F., & Welke, P. (2025). Splitting Stump Forests: Tree Ensemble Compression for Edge Devices. In D. Pedreschi, A. Monreale, R. Guidotti, R. Pellungrini, & F. Naretto (Eds.), Discovery Science: 27th International Conference, DS 2024 Pisa, Italy, October 14–16, 2024 Proceedings, Part II (pp. 3–18). Springer Nature. https://doi.org/10.34726/8839 ( reposiTUm)

Pluska, A., Welke, P., Gärtner, T., & Malhotra, S. (2024). Logical Distillation of Graph Neural Networks. In ICML 2024 Workshop on Mechanistic Interpretability. ICML 2024 Workshop on Mechanistic Interpretability, Vienna, Austria. https://doi.org/10.34726/7099 ( reposiTUm)

Pluska, A., Welke, P., Gärtner, T., & Malhotra, S. (2024). Logical Distillation of Graph Neural Networks. In P. Marquis, M. Ortiz, & M. Pagnucco (Eds.), Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning (pp. 920–930). IJCAI Organization. https://doi.org/10.24963/kr.2024/86 ( reposiTUm)

Jogl, F., Welke, P., & Gärtner, T. (2024). Is Expressivity Essential for the Predictive Performance of Graph Neural Networks? In NeurIPS 2024 Workshop on Scientific Methods for Understanding Deep Learning. NeurIPS 2024 The Thirty-Eighth Annual Conference on Neural Information Processing Systems, Vancouver, Canada. ( reposiTUm)

Graziani, C., Drucks, T., Jogl, F., Bianchini, M., Scarselli, F., & Gärtner, T. (2024). The Expressive Power of Path-Based Graph Neural Networks. In Proceedings of the 41st International Conference on Machine Learning. International Conference on Machine Learning (2024), Vienna, Austria. PMLR. http://hdl.handle.net/20.500.12708/199519 ( reposiTUm)

Chen, F., & Gärtner, T. (2024). Scalable Interactive Data Visualization. In A. Bifet, P. Daniusis, & J. Davis (Eds.), Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track : European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9–13, 2024, Proceedings, Part VIII (pp. 429–433). Springer. https://doi.org/10.1007/978-3-031-70371-3_34 ( reposiTUm)

Welke, P., Thiessen, M., Jogl, F., & Gärtner, T. (2023). Expectation-Complete Graph Representations with Homomorphisms. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning (pp. 36910–36925). Proceedings of Machine Learning Research. ( reposiTUm)

Toborek, V., Busch, M., Boßert, M., Bauckhage, C., & Welke, P. (2023). A New Aligned Simple German Corpus. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (pp. 11393–11412). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.638 ( reposiTUm)

Sanchez, R., Conrads, L., Welke, P., Cvejoski, K., & Ojeda, C. (2023). Hidden Schema Networks. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 4764–4798). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.263 ( reposiTUm)

Bause, F., Jogl, F., Indri, P., Drucks, T., Penz, D., Kriege, N., Gärtner, T., Welke, P., & Thiessen, M. (2023). Maximally Expressive GNNs for Outerplanar Graphs. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning. NeurIPS 2023 Workshop: New Frontiers in Graph Learning, New Orleans, LA, United States of America (the). OpenReview.net. https://doi.org/10.34726/5433 ( reposiTUm)

Bause, F., Jogl, F., Welke, P., & Thiessen, M. (2023). Maximally Expressive GNNs for Outerplanar Graphs. In The Second Learning on Graphs Conference (LoG 2023). Second Learning on Graphs Conference (LoG 2023), Austria. OpenReview.net. https://doi.org/10.34726/5434 ( reposiTUm)

Lachi, V., Moallemy-Oureh, A., Roth, A., & Welke, P. (2023). Graph Pooling Provably Improves Expressivity. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning. NeurIPS 2023 Workshop: New Frontiers in Graph Learning, New Orleans, LA, United States of America (the). OpenReview.net. https://doi.org/10.34726/5432 ( reposiTUm)

Müller, S., Toborek, V., Beckh, K., Jakobs, M., Bauckhage, C., & Welke, P. (2023). An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning. In D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, & F. Bonchi (Eds.), Machine Learning and Knowledge Discovery in Databases: Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III (pp. 462–478). Springer. https://doi.org/10.1007/978-3-031-43418-1_28 ( reposiTUm)

Welke, P. (2024, November 25). Expressive Graph Representations via Homomorphisms [Presentation]. CAIML Seminar, Wien, Austria. ( reposiTUm)

Welke, P. (2024, November 29). Expressive Graph Representations via Homomorphisms [Keynote Presentation]. LoG Paris Meetup, Paris, France. https://doi.org/10.34726/7501 ( reposiTUm)

Paolino, R., Maskey, S., Welke, P., & Kutyniok, G. (2024, May 11). Weisfeiler and Leman go Loopy: A New Hierarchy for Graph Representational Learning [Poster Presentation]. ICLR 2024 Workshop Bridging the Gap Between Practice and Theory in Deep Learning, Austria. https://doi.org/10.34726/6959 ( reposiTUm)

Bause, F., Jogl, F., Indri, P., Drucks, T., Penz, D., Kriege, N., Gärtner, T., Welke, P., & Thiessen, M. (2023, December 1). Maximally Expressive GNNs for Outerplanar Graphs [Poster Presentation]. Learning-on-Graphs Conference 2023: Local Meetup, München, Germany. https://doi.org/10.34726/5344 ( reposiTUm)