Alexander Beiser

Projektass. Dipl.-Ing. / BSc

Role

PreDoc Researcher
Databases and Artificial Intelligence, E192-02

Contact

Publications

Data Augmentation Techniques to Reverse-Engineer Neural Network Weights from Input-Output Queries / Beiser, A., Martinelli, F., Gerstner, W., & Brea, J. (2025). Data Augmentation Techniques to Reverse-Engineer Neural Network Weights from Input-Output Queries. arXiv. https://doi.org/10.48550/arXiv.2511.20312
Automated Hybrid Grounding Using Structural and Data-Driven Heuristics / Beiser, A., Hecher, M., & Woltran, S. (2025, November 24). Automated Hybrid Grounding Using Structural and Data-Driven Heuristics [Conference Presentation]. TAASP 2025 : Workshop on Trends and Applications of Answer Set Programming, Wien, Austria.
Automated Hybrid Grounding Using Structural and Data-Driven Heuristics / Beiser, A., Woltran, S., & Hecher, M. (2025). Automated Hybrid Grounding Using Structural and Data-Driven Heuristics. Theory and Practice of Logic Programming, 25(4), 489–506. https://doi.org/10.1017/S1471068425100173
Project: QuantASP (2025–2028)
FastFound: Easing the ASP Bottleneck via Predicate-Decoupled Grounding / Beiser, A., Gebser, M., Hecher, M., & Woltran, S. (2025). FastFound: Easing the ASP Bottleneck via Predicate-Decoupled Grounding. In M. Ortiz, R. Wassermann, & T. Schaub (Eds.), Proceedings of the TwentySecond International Conference on Principles of Knowledge Representation and Reasoning (pp. 100–109). IJCAI Organization. https://doi.org/10.24963/kr.2025/10
Intermediate Languages Matter: Formal Languages and LLMs affect Neurosymbolic Reasoning / Beiser, A., Penz, D., & Musliu, N. (2025). Intermediate Languages Matter: Formal Languages and LLMs affect Neurosymbolic Reasoning. In D. Chaves-Fraga, I. Heibi, D. Garijo, D. Collarana, A. Salatino, & S. Vahdati (Eds.), Joint Proceedings of Posters, Demos, Workshops, and Tutorials of the 21st International Conference on Semantic Systems co-located with 21st International Conference on Semantic Systems (SEMANTiCS 2025). CEUR.
Project: BILAI (2024–2029)
Novel techniques for circumventing the ASP Bottleneck / Beiser, A. (2025). Novel techniques for circumventing the ASP Bottleneck [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.127965
Download: PDF (6.01 MB)
Bypassing the ASP Bottleneck: Hybrid Grounding by Splitting and Rewriting / Beiser, A., Hecher, M., Unalan, K., & Woltran, S. (2024). Bypassing the ASP Bottleneck: Hybrid Grounding by Splitting and Rewriting. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (pp. 3250–3258). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2024/360
Project: REVEAL-AI (2020–2024)