2024W

• Introduction to Machine Learning / 194.025 / VU
• Machine Learning Algorithms and Applications / 194.101 / PR
• Seminar for PhD Students / 194.110 / SE
• Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
• Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU

2025S

• Seminar for PhD Students / 194.110 / SE

• Structured Data Learning with Generalized Similarities
  2023 – 2027 / Vienna Science and Technology Fund (WWTF)
  Publications: 188226 / 188931 / 188939 / 188937 / 191403 / 193749 / 193748 / 193747 / 199519 / 203813 / 203184 / 204922 / 205365 / 205196

• Expressive Graph Representations via Homomorphisms / Welke, P. (2024, November 29). Expressive Graph Representations via Homomorphisms [Keynote Presentation]. LoG Paris Meetup, Paris, France. https://doi.org/10.34726/7501
  Download: presentation slides (4.44 MB)
  Project: StruDL (2023–2027)
• Expressive Graph Representations via Homomorphisms / Welke, P. (2024, November 25). Expressive Graph Representations via Homomorphisms [Presentation]. CAIML Seminar, Wien, Austria.
  Project: StruDL (2023–2027)
• Logical Distillation of Graph Neural Networks / Pluska, A., Welke, P., Gärtner, T., & Malhotra, S. (2024). Logical Distillation of Graph Neural Networks. In ICML 2024 Workshop on Mechanistic Interpretability. 21st International Conference on Principles of Knowledge Representation and Reasoning, Hanoi, Viet Nam. https://doi.org/10.34726/7099
  Download: PDF (309 KB)
  Project: StruDL (2023–2027)
• Weisfeiler and Leman go Loopy: A New Hierarchy for Graph Representational Learning / 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
  Download: poster (680 KB)
  Project: StruDL (2023–2027)
• Maximally Expressive GNNs for Outerplanar Graphs / 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
  Download: PDF (880 KB)
  Project: StruDL (2023–2027)
• Maximally Expressive GNNs for Outerplanar Graphs / 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
  Downloads: Paper (880 KB) / Poster (422 KB)
  Project: StruDL (2023–2027)
• Extending Graph Neural Networks with Global Features / Brasoveanu, A. D., Jogl, F., Welke, P., & Thiessen, M. (2023, December 1). Extending Graph Neural Networks with Global Features [Poster Presentation]. Learning-on-Graphs Conference 2023: Local Meetup, München, Germany. https://doi.org/10.34726/5343
  Downloads: Paper (365 KB) / Poster (289 KB)
• Extending Graph Neural Networks with Global Features / Brasoveanu, A. D., Jogl, F., Welke, P., & Thiessen, M. (2023, November 27). Extending Graph Neural Networks with Global Features [Poster Presentation]. Learning on Graphs Conference 2023, Austria. https://doi.org/10.34726/5281
  Download: Camera-ready full paper (365 KB)
• Maximally Expressive GNNs for Outerplanar Graphs / 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
  Download: PDF (541 KB)
  Project: StruDL (2023–2027)
• Hidden Schema Networks / 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
  Project: StruDL (2023–2027)
• A New Aligned Simple German Corpus / 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
  Project: StruDL (2023–2027)
• Graph Pooling Provably Improves Expressivity / 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
  Downloads: PDF (245 KB) / Poster (210 KB)
  Project: StruDL (2023–2027)
• Extending Graph Neural Networks with Global Features / Brasoveanu, A. D., Jogl, F., Welke, P., & Thiessen, M. (2023). Extending Graph Neural Networks with Global Features. In The Second Learning on Graphs Conference (LoG 2023). The Second Learning on Graphs Conference (LoG 2023), online, Austria. OpenReview.net. https://doi.org/10.34726/5423
  Downloads: PDF (365 KB) / Poster (289 KB)
• Retention is All You Need / Mohiuddin, K., Alam, M. A., Alam, M. M., Welke, P., Martin, M., Lehmann, J., & Vahdati, S. (2023). Retention is All You Need. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 4752–4758). https://doi.org/10.1145/3583780.3615497
• An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning / 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
  Project: StruDL (2023–2027)
• Expectation-Complete Graph Representations with Homomorphisms / 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.
  Project: StruDL (2023–2027)