TU Wien Informatics

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Andre Schidler

Projektass.(FWF) Dipl.-Ing. Dr.techn. / BSc

Andre Schidler

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  • Computing optimal hypertree decompositions with SAT / Schidler, A., & Szeider, S. (2023). Computing optimal hypertree decompositions with SAT. Artificial Intelligence, 325, Article 104015. https://doi.org/10.1016/j.artint.2023.104015
    Download: PDF (1.74 MB)
    Projects: REVEAL-AI (2020–2024) / SLIM (2019–2024) / STRIDES (2023–2026)
  • SAT-boosted tabu search for coloring massive graphs / Schidler, A., & Szeider, S. (2023). SAT-boosted tabu search for coloring massive graphs. ACM Journal on Experimental Algorithmics, 28, Article 1.5. https://doi.org/10.1145/3603112
    Download: PDF (1.15 MB)
    Projects: DK - Logic (2014–2023) / REVEAL-AI (2020–2024) / SLIM (2019–2024) / STRIDES (2023–2026)
  • Computing Twin-width with SAT and Branch & Bound / Schidler, A., & Szeider, S. (2023). Computing Twin-width with SAT and Branch & Bound. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23) (pp. 2013–2021). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2023/224
    Download: PDF (263 KB)
    Projects: DK - Logic (2014–2023) / REVEAL-AI (2020–2024) / SLIM (2019–2024) / STRIDES (2023–2026)
  • Scalability for SAT-based combinatorial problem solving / Schidler, A. (2023). Scalability for SAT-based combinatorial problem solving [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.113248
    Download: PDF (6.06 MB)
  • A Dynamic MaxSAT-based Approach to Directed Feedback Vertex Sets / Kiesel, R., & Schidler, A. (2023). A Dynamic MaxSAT-based Approach to Directed Feedback Vertex Sets. In 2023 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX) (pp. 39–52). https://doi.org/10.1137/1.9781611977561.ch4
  • PACE Solver Description: DAGer – Cutting out Cycles with MaxSAT / Kiesel, R., & Schidler, A. (2022). PACE Solver Description: DAGer – Cutting out Cycles with MaxSAT. In 17th International Symposium on Parameterized and Exact Computation (IPEC 2022). 17th International Symposium on Parameterized and Exact Computation (IPEC 2022), Germany. https://doi.org/10.4230/LIPIcs.IPEC.2022.32
  • Threshold Treewidth and Hypertree Width / Ganian, R., Schidler, A., Sorge, M., & Szeider, S. (2022). Threshold Treewidth and Hypertree Width. Journal of Artificial Intelligence Research, 74, 1687–1713. https://doi.org/10.1613/JAIR.1.13661
    Download: PDF (6.1 MB)
    Projects: NFPC (2018–2022) / Parameterisierte Analyse in der Künstlichen Intelligenz (2021–2026) / REVEAL-AI (2020–2024) / SLIM (2019–2024)
  • Weighted Model Counting with Twin-Width / Ganian, R., Pokrývka, F., Schidler, A., Simonov, K., & Szeider, S. (2022). Weighted Model Counting with Twin-Width. In K. S. Meel & O. Strichman (Eds.), 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022) (pp. 1–17). Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.SAT.2022.15
    Download: PDF (719 KB)
    Projects: NFPC (2018–2022) / Parameterisierte Analyse in der Künstlichen Intelligenz (2021–2026) / REVEAL-AI (2020–2024) / SLIM (2019–2024)
  • Tractable Abstract Argumentation via Backdoor-Treewidth / Dvořák, W., Hecher, M., König, M., Schidler, A., Szeider, S., & Woltran, S. (2022). Tractable Abstract Argumentation via Backdoor-Treewidth. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (pp. 5608–5615). AAAI Press. https://doi.org/10.1609/aaai.v36i5.20501
    Download: PDF (1.52 MB)
    Projects: HYPAR (2019–2024) / REVEAL-AI (2020–2024) / SLIM (2019–2024)
  • SAT-Based Local Search for Plane Subgraph Partitions / Schidler, A. (2022). SAT-Based Local Search for Plane Subgraph Partitions. In X. Goaoc & M. Kerber (Eds.), 38th International Symposium on Computational Geometry (SoCG 2022) (pp. 1–8). Schloss Dagstuhl --Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.SoCG.2022.74
    Download: PDF (865 KB)
    Projects: REVEAL-AI (2020–2024) / SLIM (2019–2024)
  • A SAT Approach to Twin-Width / Schidler, A., & Szeider, S. (2022). A SAT Approach to Twin-Width. In 2022 Proceedings of the Symposium on Algorithm Engineering and Experiments (ALENEX) (pp. 67–77). https://doi.org/10.1137/1.9781611977042.6
  • Computing Optimal Hypertree Decompositions with SAT / Schidler, A., & Szeider, S. (2021). Computing Optimal Hypertree Decompositions with SAT. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. IJCAI 2021 - 30th International Joint Conference on Artificial Intelligence, Montreal, Canada, Canada. https://doi.org/10.24963/ijcai.2021/196
  • SAT-based Decision Tree Learning for Large Data Sets / Schidler, A., & Szeider, S. (2021). SAT-based Decision Tree Learning for Large Data Sets. In Thirty-Fifth AAAI Conference on Artificial Intelligence (pp. 3904–3912). AAAI Press. http://hdl.handle.net/20.500.12708/58603
  • Threshold Treewidth and Hypertree Width / Ganian, R., Schidler, A., Sorge, M., & Szeider, S. (2021). Threshold Treewidth and Hypertree Width. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. IJCAI’20: Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan. https://doi.org/10.24963/ijcai.2020/263
  • Computing Optimal Hypertree Decompositions / Schidler, A., & Szeider, S. (2020). Computing Optimal Hypertree Decompositions. In 2020 Proceedings of the Twenty-Second Workshop on Algorithm Engineering and Experiments (ALENEX) (pp. 1–11). siam. https://doi.org/10.1137/1.9781611976007.1
  • Solving the Steiner Tree Problem with few Terminals / Fichte, J. K., Hecher, M., & Schidler, A. (2020). Solving the Steiner Tree Problem with few Terminals. In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI). ICTAI 2020 - 32th International Conference on Tools with Artificial Intelligence, online conference, Unknown. https://doi.org/10.1109/ictai50040.2020.00054
    Projects: HYPAR (2019–2024) / START (2014–2022)
  • Towards Faster Reasoners by Using Transparent Huge Pages / Fichte, J., Manthey, N., Stecklina, J., & Schidler, A. (2020). Towards Faster Reasoners by Using Transparent Huge Pages. In H. Simonis (Ed.), Principles and Practice of Constraint Programming CP 2020 (pp. 304–322). Springer Cham. https://doi.org/10.1007/978-3-030-58475-7_18
  • A solver for the Steiner tree problem with few terminals / Schidler, A. (2018). A solver for the Steiner tree problem with few terminals [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2018.55931
    Download: PDF (991 KB)