TU Wien Informatics

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Marcel Moosbrugger

Univ.Ass. Dipl.-Ing. / BSc BA (Hons)

Marcel Moosbrugger

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2023W

2024S

 

  • Strong Invariants Are Hard: On the Hardness of Strongest Polynomial Invariants for (Probabilistic) Programs / Müllner, J., Moosbrugger, M., & Kovács, L. (2024). Strong Invariants Are Hard: On the Hardness of Strongest Polynomial Invariants for (Probabilistic) Programs. Proceedings of the ACM on Programming Languages, 8(POPL), 882–910. https://doi.org/10.1145/3632872
  • Automated Sensitivity Analysis for Probabilistic Loops / Moosbrugger, M., Müllner, J., & Kovács, L. (2023). Automated Sensitivity Analysis for Probabilistic Loops. In P. Herber & A. Wijs (Eds.), iFM 2023 : 18th International Conference, iFM 2023, Leiden, The Netherlands, November 13–15, 2023, Proceedings (pp. 21–39). Springer. https://doi.org/10.1007/978-3-031-47705-8_2
  • This Is the Moment for Probabilistic Loops / Moosbrugger, M., Stankovic, M., Bartocci, E., & Kovacs, L. (2022). This Is the Moment for Probabilistic Loops. Proceedings of the ACM on Programming Languages, 6(OOPSLA2), 1497–1525. https://doi.org/10.1145/3563341
  • Distribution Estimation for Probabilistic Loops / Karimi, A., Moosbrugger, M., Stankovič, M., Kovács, L., Bartocci, E., & Bura, E. (2022). Distribution Estimation for Probabilistic Loops. In E. Ábrahám & M. Paolieri (Eds.), Quantitative Evaluation of Systems (pp. 26–42). Springer-Verlag. https://doi.org/10.1007/978-3-031-16336-4_2
    Projects: ARTIST (2021–2026) / ProbInG (2020–2025)
  • The probabilistic termination tool amber / Moosbrugger, M., Bartocci, E., Katoen, J.-P., & Kovacs, L. (2022). The probabilistic termination tool amber. Formal Methods in System Design, 61(1), 90–109. https://doi.org/10.1007/s10703-023-00424-z
    Projects: ARTIST (2021–2026) / ProbInG (2020–2025)
  • Moment-Based Invariants for Probabilistic Loops with Non-polynomial Assignments / Kofnov, A., Moosbrugger, M., Stankovič, M., Bartocci, E., & Bura, E. (2022). Moment-Based Invariants for Probabilistic Loops with Non-polynomial Assignments. In E. Ábrahám & M. Paolieri (Eds.), Quantitative Evaluation of Systems (pp. 3–25). Springer. https://doi.org/10.1007/978-3-031-16336-4_1
    Projects: ARTIST (2021–2026) / ProbInG (2020–2025)
  • Solving Invariant Generation for Unsolvable Loops / Amrollahi, D., Bartocci, E., Kenison, G., Kovács, L., Moosbrugger, M., & Stankovič, M. (2022). Solving Invariant Generation for Unsolvable Loops. In Static Analysis: 29th International Symposium, SAS 2022 (pp. 19–43). https://doi.org/10.1007/978-3-031-22308-2_3
  • The Probabilistic Termination Tool Amber / Marcel Moosbrugger, Ezio Bartocci, Katoen, J.-P., & Laura Kovács. (2021). The Probabilistic Termination Tool Amber. In Formal Methods. FM 2021 (pp. 667–675). https://doi.org/10.1007/978-3-030-90870-6_36
  • Automated Termination Analysis of Polynomial Probabilistic Programs / Moosbrugger, M., Bartocci, E., Katoen, J.-P., & Kovács, L. (2021). Automated Termination Analysis of Polynomial Probabilistic Programs. In Programming Languages and Systems (pp. 491–518). Springer. https://doi.org/10.1007/978-3-030-72019-3_18
  • Automating termination analysis of probabilistic programs / Moosbrugger, M. (2020). Automating termination analysis of probabilistic programs [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2020.77501
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