2024W

• 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

• Doktorand/inn/enprogramm der ÖAW
  2024 – 2026 / Austrian Academy of Sciences

• Bandits with Abstention under Expert Advice / Pasteris, S., Rumi, A., Thiessen, M., Saito, S., Miyauchi, A., Vitale, F., & Herbster, M. (2024, June 17). Bandits with Abstention under Expert Advice [Poster Presentation]. ICML 2024 Workshop: Foundations of Reinforcement Learning and Control -- Connections and Perspectivesing (ICML 2024), Vienna, Austria. http://hdl.handle.net/20.500.12708/199835
• A Theory of Interpretable Approximations / Bressan, M., Cesa-Bianchi, N., Esposito, E., Mansour, Y., Moran, S., & Thiessen, M. (2024). A Theory of Interpretable Approximations. In S. Agrawal & A. Roth (Eds.), Proceedings of Thirty Seventh Conference on Learning Theory. http://hdl.handle.net/20.500.12708/199886
  Download: PDF (344 KB)
• Efficient Algorithms for Learning Monophonic Halfspaces in Graphs / Bressan, M., Esposito, E., & Thiessen, M. (2024). Efficient Algorithms for Learning Monophonic Halfspaces in Graphs. In Proceedings of Machine Learning Research. 37th Annual Conference on Learning Theory (2024), Edmonton, Canada. http://hdl.handle.net/20.500.12708/199834
• 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)
• 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)
• Generalized Laplacian Positional Encoding for Graph Representation Learning / Maskey, S., Parviz, A., Thiessen, M., Stärk, H., Sadikaj, Y., & Maron, H. (2022, December 3). Generalized Laplacian Positional Encoding for Graph Representation Learning [Poster Presentation]. NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, New Orleans, United States of America (the). https://doi.org/10.34726/3908
  Download: Paper (1.74 MB)
• Expectation Complete Graph Representations Using Graph Homomorphisms / Welke, P., Thiessen, M., & Gärtner, T. (2022, November 30). Expectation Complete Graph Representations Using Graph Homomorphisms [Poster Presentation]. First Learning on Graphs Conference (LoG 2022), Unknown. https://doi.org/10.34726/3883
  Download: Accepted Paper (294 KB)
• Active Learning of Classifiers with Label and Seed Queries / Bressan, M., Cesa-Bianchi, N., Lattanzi, S., Paudice, A., & Thiessen, M. (2022). Active Learning of Classifiers with Label and Seed Queries. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, Louisiana, United States of America (the). Neural information processing systems foundation. https://doi.org/10.34726/4021
  Downloads: Full paper (387 KB) / Supplemantary material (294 KB)
• Expectation Complete Graph Representations using Graph Homomorphisms / Thiessen, M., Pascal Welke, & Gärtner, T. (2022, October 25). Expectation Complete Graph Representations using Graph Homomorphisms [Presentation]. Workshop: Hot Topics in Graph Neural Networks, Kassel, Germany. http://hdl.handle.net/20.500.12708/135860
  Download: slides of invited talk (1.26 MB)
• Expectation Complete Graph Representations Using Graph Homomorphisms / Thiessen, M., Welke, P., & Gärtner, T. (2022, October 21). Expectation Complete Graph Representations Using Graph Homomorphisms [Poster Presentation]. New Frontiers in Graph Learning (GLFrontiers) NeurIPS 2022 Workshop, New Orleans, United States of America (the). https://doi.org/10.34726/3863
  Download: Full paper (304 KB)
• 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)
• Online learning of convex sets on graphs / Thiessen, M., & Gärtner, T. (2022). Online learning of convex sets on graphs. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022), Grenoble, France.
• Active Learning Convex Halfspaces on Graphs / Thiessen, M., & Gärtner, T. (2021). Active Learning Convex Halfspaces on Graphs. In SubSetML @ ICML2021: Subset Selection in Machine Learning: From Theory to Practice. SubSetML: Subset Selection in Machine Learning: From Theory to Practice, Unknown. https://doi.org/10.34726/3901
  Download: Accepted full paper with appendix (2.27 MB)
• Active Learning of Convex Halfspaces on Graphs / Thiessen, M., & Gärtner, T. (2021). Active Learning of Convex Halfspaces on Graphs. In Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (pp. 1–13). https://doi.org/10.34726/1841
  Download: PDF (1.06 MB)
• Active Learning of Convex Halfspaces on Graphs / Thiessen, M., & Gärtner, T. (2021). Active Learning of Convex Halfspaces on Graphs. In Advances in Neural Information Processing Systems 34. Advances in Neural Information Processing Systems 34. http://hdl.handle.net/20.500.12708/58787
• Efficient Reinforcement Learning via Self-supervised learning and Model-based methods / Schmied, T., & Thiessen, M. (2020). Efficient Reinforcement Learning via Self-supervised learning and Model-based methods. In Challenges of Real-World RL. NeurIPS 2020 Workshop. Accepted Papers. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. https://doi.org/10.34726/4524
  Download: Accepted paper (236 KB)
• Active Learning on Graphs with Geodesically Convex Classes / Thiessen, M., & Gärtner, T. (2020). Active Learning on Graphs with Geodesically Convex Classes. In Proceedings of 16th International Workshop on Mining and Learning with Graphs (MLG’20). 16th International Workshop on Mining and Learning with Graphs, Austria. https://doi.org/10.34726/3467
  Download: author's original (729 KB)

• 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)