TU Wien Informatics

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Martin Tappler

Projektass. Dr.techn.

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Contact

  • Rule-Guided Reinforcement Learning Policy Evaluation and Improvement / Tappler, M., Lopez-Miguel, I. D., Tschiatschek, S., & Bartocci, E. (2025). Rule-Guided Reinforcement Learning Policy Evaluation and Improvement. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (pp. 6254–6262). https://doi.org/10.24963/ijcai.2025/696
    Project: TAIGER (2023–2027)
  • Message from the A-MOST 2025 Workshop Chairs / Lorber, F., Seceleanu, C., & Tappler, M. (2025). Message from the A-MOST 2025 Workshop Chairs. In 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) (pp. xii–xiii). https://doi.org/10.1109/ICSTW64639.2025.10962506
  • Mutating Skeletons: Learning Timed Automata via Domain Knowledge / Wallner, F., Aichernig, B., Lorber, F., & Tappler, M. (2025). Mutating Skeletons: Learning Timed Automata via Domain Knowledge. In 2025 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) (pp. 67–77). https://doi.org/10.1109/ICSTW64639.2025.10962513
    Project: TAIGER (2023–2027)
  • Hierarchical Learning of Generative Automaton Models from Sequential Data / von Berg, B., Aichernig, B. K., Rindler, M., Štern, D., & Tappler, M. (2024). Hierarchical Learning of Generative Automaton Models from Sequential Data. In Software Engineering and Formal Methods (pp. 215–233). https://doi.org/10.1007/978-3-031-77382-2_13
  • On the Relationship Between RNN Hidden-State Vectors and Semantic Structures / Muskardin, E., Tappler, M., Pill, I., Aichernig, B., & Pock, T. (2024). On the Relationship Between RNN Hidden-State Vectors and Semantic Structures. In Findings of the Association for Computational Linguistics ACL 2024 (pp. 5641–5658). https://doi.org/10.18653/v1/2024.findings-acl.335
    Project: TAIGER (2023–2027)
  • Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning / Pranger, S., Chockler, H., Tappler, M., & Könighofer, B. (2024). Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning. In A. Globerson, L. Mackey, & D. Belgrave (Eds.), Advances in Neural Information Processing Systems 37 (NeurIPS 2024). http://hdl.handle.net/20.500.12708/213273
    Project: TAIGER (2023–2027)