Simon Wietheger

Projektass.(FWF) / MSc

Simon Wietheger

Role

PreDoc Researcher
Algorithms and Complexity, E192-01

Contact

Publications

Clustering Permutations Under the Ulam Metric: A Parameterized Complexity Study / Bai, T., Fomin, F. V., Golovach, P. A., More, Y. H., & Wietheger, S. (2026). Clustering Permutations Under the Ulam Metric: A Parameterized Complexity Study. In S. Bhattacharya, D. Nanongkai, M. Benedikt, & G. Puppis (Eds.), 53rd International Colloquium on Automata, Languages, and Programming (ICALP 2026). Schloss Dagstuhl. https://doi.org/10.4230/LIPIcs.ICALP.2026.19
Matrix Editing Meets Fair Clustering: Parameterized Algorithms and Complexity / Ganian, R., Hoang, H. P., & Wietheger, S. (2026). Matrix Editing Meets Fair Clustering: Parameterized Algorithms and Complexity. In Fortieth AAAI Conference on Artificial Intelligence Thirty-Eighth Conference on Innovative Applications of Artificial Intelligence (pp. 19108–19116). AAAI Press. https://doi.org/10.1609/aaai.v40i23.38984
A Structural Complexity Analysis of Hierarchical Task Network Planning / Brand, C., Ganian, R., Mc Inerney, F., & Wietheger, S. (2025). A Structural Complexity Analysis of Hierarchical Task Network Planning. In J. Kwok (Ed.), Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (pp. 4391–4400). https://doi.org/10.24963/ijcai.2025/489
Training One-Dimensional Graph Neural Networks is NP-Hard / Ganian, R., Rocton, M., & Wietheger, S. (2025). Training One-Dimensional Graph Neural Networks is NP-Hard. In The Thirteenth International Conference on Learning Representations : ICLR 2025. Thirteenth International Conference on Learning Representations, Singapore.
Near-Tight Runtime Guarantees for Many-Objective Evolutionary Algorithms / Wietheger, S., & Doerr, B. (2024). Near-Tight Runtime Guarantees for Many-Objective Evolutionary Algorithms. In Parallel Problem Solving from Nature – PPSN XVIII : 18th International Conference, PPSN 2024, Hagenberg, Austria, September 14–18, 2024, Proceedings, Part IV (pp. 153–168). Springer. https://doi.org/10.1007/978-3-031-70085-9_10
Project: Parameterisierte Analyse in der Künstlichen Intelligenz (2021–2027)
A Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) / Wietheger, S., & Doerr, B. (2024). A Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm III (NSGA-III). In GECCO ’24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 63–64). The Association for Computing Machinery. https://doi.org/10.1145/3638530.3664062
Hot off the Press: The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem / Cerf, S., Doerr, B., Hebras, B., Kahane, Y., & Wietheger, S. (2024). Hot off the Press: The First Proven Performance Guarantees for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) on a Combinatorial Optimization Problem. In GECCO ’24 Companion: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 27–28). The Association for Computing Machinery. https://doi.org/10.1145/3638530.3664080