Research Interests
- Formal Methods for Machine Learning in the context of topics like:
- Neural Network Verification
- Verification of ML-enabled Cyber-Physical Systems
- Algorithmic Fairness
- Quantitative Verification of Software
Publications
| Titel | Autor(en) | Quelle |
|---|---|---|
| Quantifying Software Correctness by Combining Architecture Modeling and Formal Program Analysis | Florian Lanzinger, Christian Martin, Frederik Reiche, Samuel Teuber, Robert Heinrich und Alexander Weigl | Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, SAC 2024 |
| Titel | Autor(en) | Quelle |
|---|---|---|
| The counterSharp Model Counting Benchmark | Samuel Teuber und Alexander Weigl | Karlsruhe Institute of Technology 2022-02 |
| Titel | Autor(en) | Quelle |
|---|---|---|
| Geometric Path Enumeration for Equivalence Verification of Neural Networks | Samuel Teuber, Marko Kleine Büning, Philipp Kern und Carsten Sinz | 33rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2021) |
| Quantifying Software Reliability via Model-Counting | Samuel Teuber und Alexander Weigl | 18th International Conference on Quantitative Evaluation of Systems (QEST 2021) |
| Titel | Autor(en) | Quelle |
|---|---|---|
| An Incremental Abstraction Scheme for Solving Hard SMT-Instances over Bit-Vectors | Samuel Teuber, Marko Kleine Büning und Carsten Sinz | CoRR abs/2008.10061 |
| Titel | Autor(en) | Quelle |
|---|---|---|
| Efficient unpacking of required software from CERNVM-FS | Samuel Teuber | Zenodo 2019-02 |