2024W

• Introduction to Machine Learning / 194.025 / VU
• Machine Learning Algorithms and Applications / 194.101 / PR
• Project in Computer Science 1 / 194.145 / PR
• Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
• Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU

2025S

• Algorithms and Data Structures / 186.866 / VU
• Introduction to Machine Learning / 194.025 / VU
• Machine Learning Algorithms and Applications / 194.101 / PR
• Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
• Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU

• The Expressive Power of Path-Based Graph Neural Networks / Graziani, C., Drucks, T., Jogl, F., Bianchini, M., Scarselli, F., & Gärtner, T. (2024). The Expressive Power of Path-Based Graph Neural Networks. In Z. K. Ruslan Salakhutdinov Katherine Heller, Adrian Weller, Nuria Oliver, Jonathan Scarlett, Felix Berkenkamp (Ed.), Proceedings of the 41st International Conference on Machine Learning. PMLR. http://hdl.handle.net/20.500.12708/199519
  Download: PDF (1.68 MB)
  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)
• 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)
• 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, 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)
• Expressivity-Preserving GNN Simulation / Jogl, F., Thiessen, M., & Gärtner, T. (2023). Expressivity-Preserving GNN Simulation. In Advances in Neural Information Processing Systems. 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, United States of America (the).
• 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)
• 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)
• Weisfeiler and Leman Return with Graph Transformations / Jogl, F., Thiessen, M., & Gärtner, T. (2022). Weisfeiler and Leman Return with Graph Transformations. In 18th International Workshop on Mining and Learning with Graphs - Accepted Papers. 18th International Workshop on Mining and Learning with Graphs, Grenoble, France. https://doi.org/10.34726/3829
  Download: Full paper as PDF (439 KB)
• Reducing Learning on Cell Complexes to Graphs / Jogl, F., Thiessen, M., & Gärtner, T. (2022). Reducing Learning on Cell Complexes to Graphs. In ICLR 2022 Workshop on Geometrical and Topological Representation Learning. ICLR 2022 Workshop on Geometrical and Topological Representation Learning, Unknown. https://doi.org/10.34726/3421
  Download: Paper as PDF (263 KB)
• Historian: A Large-Scale Historical Film Dataset with Cinematographic Annotation / Helm, D., Jogl, F., & Kampel, M. (2022). Historian: A Large-Scale Historical Film Dataset with Cinematographic Annotation. In 2022 IEEE International Conference on Image Processing (ICIP) (pp. 2087–2091). https://doi.org/10.1109/ICIP46576.2022.9897300
  Project: VHH (2019–2023)
• Do we need to Improve message passing? Improving graph neural networks with graph transformations / Jogl, F. (2022). Do we need to Improve message passing? Improving graph neural networks with graph transformations [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.103141
  Download: PDF (897 KB)
• On (Coalitional) Exchange-Stable Matching / Chen, J., Chmurovic, A., Jogl, F., & Sorge, M. (2021). On (Coalitional) Exchange-Stable Matching. In Algorithmic Game Theory (pp. 205–220). LNCS / Springer. https://doi.org/10.1007/978-3-030-85947-3_14