Maximilian Thiessen
Univ.Ass. / MSc
Role
-
PreDoc Researcher
Machine Learning, E194-06
Courses
2022W
- Bachelor Thesis in Computer Science / 194.112 / PR
- Machine Learning Algorithms and Applications / 194.101 / PR
- Project in Computer Science 1 - Machine Learning Algorithms and Applications / 194.119 / PR
- Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
- Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU
2023S
- Bachelor Thesis in Computer Science / 194.112 / PR
- Machine Learning Algorithms and Applications / 194.101 / PR
- Project in Computer Science 1 - Machine Learning Algorithms and Applications / 194.119 / PR
- Seminar in Artificial Intelligence - Theoretical Aspects of Machine Learning / 194.118 / SE
- Theoretical Foundations and Research Topics in Machine Learning / 194.100 / VU
Publications
Note: Due to the rollout of TU Wien’s new publication database, the list below may be slightly outdated. Once the migration is complete, everything will be up to date again.
- 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.
- 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
- 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, International. https://doi.org/10.34726/3421
- 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 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
- 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
- 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
Supervisions
Note: Due to the rollout of TU Wien’s new publication database, the list below may be slightly outdated. Once the migration is complete, everything will be up to date again.
- 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