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Mathis Teva Rocton

Univ.Ass. / MSc

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  • Polynomial kernels for edge modification problems towards block and strictly chordal graphs / Dumas, M., Perez, A., Rocton, M., & Todinca, I. (2025). Polynomial kernels for edge modification problems towards block and strictly chordal graphs. DISCRETE MATHEMATICS AND THEORETICAL COMPUTER SCIENCE, 27:2(Discrete Algorithms), Article 5. https://doi.org/10.46298/dmtcs.12998
  • The Computational Complexity of Positive Non-Clashing Teaching in Graphs / Ganian, R., Khazaliya, L., Rocton, M., & Mc Inerney, F. (2025). The Computational Complexity of Positive Non-Clashing Teaching in Graphs. In The Thirteenth International Conference on Learning Representations : ICLR 2025. Thirteenth International Conference on Learning Representations (ICLR 2025), Singapore. International Conference on Learning Representations (ICLR).
  • 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.
  • Twin-Width Meets Feedback Edges and Vertex Integrity / Balabán, J., Ganian, R., & Rocton, M. T. (2024). Twin-Width Meets Feedback Edges and Vertex Integrity. In 19th International Symposium on Parameterized and Exact Computation (IPEC 2024). International Symposium on Parameterized and Exact Computation (IPEC 2024), Egham, United Kingdom of Great Britain and Northern Ireland (the). Schloss Dagstuhl – Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.IPEC.2024.3
    Projects: Parameterisierte Analyse in der Künstlichen Intelligenz (2021–2026) / Parameterized Graph Drawing (2023–2027)
  • Computing Twin-Width Parameterized by the Feedback Edge Number / Balabán, J., Ganian, R., & Rocton, M. (2024). Computing Twin-Width Parameterized by the Feedback Edge Number. In 41st International Symposium on Theoretical Aspects of Computer Science (STACS 2024) (pp. 7:1-7:19). Schloss Dagstuhl – Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.STACS.2024.7
    Projects: Parameterisierte Analyse in der Künstlichen Intelligenz (2021–2026) / Parameterized Graph Drawing (2023–2027)
  • PACE Solver Description: Touiouidth / Berthe, G., Codert-Osman, Y., Dobler, A., Morelle, L., Reinald, A., & Rocton, M. (2023). PACE Solver Description: Touiouidth. In N. Misra & M. Wahlström (Eds.), 18th International Symposium on Parameterized and Exact Computation (IPEC 2023) (pp. 38:1-38:4). Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. https://doi.org/10.4230/LIPIcs.IPEC.2023.38
  • New Complexity-Theoretic Frontiers of Tractability for Neural Network Training / Brand, C., Ganian, R., & Rocton, M. T. (2023). New Complexity-Theoretic Frontiers of Tractability for Neural Network Training. In 37th Conference on Neural Information Processing Systems (NeurIPS 2023). NeurIPS 2023: Thirty-seventh Annual Conference on Neural Information Processing Systems, New Orleans, United States of America (the).
  • Turbocharging twin-width heuristics with SAT / Jäger, D. (2024). Turbocharging twin-width heuristics with SAT [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.125321
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