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Chalupa, M., Mühlböck, F., Muroya Lei, S., &; Henzinger, T. A. (2023). VAMOS: Middleware for Best-Effort Third-Party Monitoring. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:12407 

Chatterjee, K., Henzinger, T. A., Lechner, M., &; Zikelic, D. (2023). A learner-verifier framework for neural network controllers and certificates of stochastic systems. In Tools and Algorithms for the Construction and Analysis of Systems  (Vol. 13993, pp. 3–25). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-30823-9_1 

Anand, A., Mallik, K., Nayak, S. P., &; Schmuck, A. K. (2023). Computing adequately permissive assumptions for synthesis. In TACAS 2023: Tools and Algorithms for the Construction and Analysis of Systems (Vol. 13994, pp. 211–228). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-30820-8_15 

Henzinger, T. A., Mazzocchi, N. A., &; Sarac, N. E. (2023). Quantitative safety and liveness. In 26th International Conference Foundations of Software Science and Computation Structures (Vol. 13992, pp. 349–370). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-30829-1_17 

Henzinger, T. A., Kebis, P., Mazzocchi, N. A., &; Sarac, N. E. (2023). Regular methods for operator precedence languages. In 50th International Colloquium on Automata, Languages, and Programming (Vol. 261, p. 129:1--129:20). Paderborn, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.ICALP.2023.129 

Lechner, M., Amini, A., Rus, D., &; Henzinger, T. A. (2023). Revisiting the adversarial robustness-accuracy tradeoff in robot learning. IEEE Robotics and Automation Letters. Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/LRA.2023.3240930 

Benes, N., Brim, L., Huvar, O., Pastva, S., &; Safránek, D. (2023). Boolean network sketches: A unifying framework for logical model inference. Bioinformatics. Oxford Academic.https://doi.org/10.1093/bioinformatics/btad158 

Lechner, M., Zikelic, D., Chatterjee, K., Henzinger, T. A., &; Rus, D. (2023). Quantization-aware interval bound propagation for training certifiably robust quantized neural networks. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 14964–14973). Washington, DC, United States: Association for the Advancement of Artificial Intelligence.https://doi.org/10.1609/aaai.v37i12.26747 

Avni, G., Jecker, I. R., &; Zikelic, D. (2023). Bidding graph games with partially-observable budgets. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 5464–5471). Washington, DC, United States.https://doi.org/10.1609/aaai.v37i5.25679 

Henzinger, T. A., Karimi, M., Kueffner, K., &; Mallik, K. (2023). Monitoring algorithmic fairness. In Computer Aided Verification (Vol. 13965, pp. 358–382). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-37703-7_17 

Boker, U., Henzinger, T. A., Mazzocchi, N. A., &; Sarac, N. E. (2023). Safety and liveness of quantitative automata. In 34th International Conference on Concurrency Theory (Vol. 279). Antwerp, Belgium: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.CONCUR.2023.17 

Bartocci, E., Henzinger, T. A., Nickovic, D., &; Oliveira da Costa, A. (2023). Hypernode automata. In 34th International Conference on Concurrency Theory (Vol. 279). Antwerp, Belgium: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.CONCUR.2023.21 

Henzinger, T. A., Kueffner, K., &; Mallik, K. (2023). Monitoring algorithmic fairness under partial observations. In 23rd International Conference on Runtime Verification (Vol. 14245, pp. 291–311). Thessaloniki, Greece: Springer Nature.https://doi.org/10.1007/978-3-031-44267-4_15 

Avni, G., Meggendorfer, T., Sadhukhan, S., Tkadlec, J., &; Zikelic, D. (2023). Reachability poorman discrete-bidding games. In Frontiers in Artificial Intelligence and Applications (Vol. 372, pp. 141–148). Krakow, Poland: IOS Press.https://doi.org/10.3233/FAIA230264 

Ansaripour, M., Chatterjee, K., Henzinger, T. A., Lechner, M., &; Zikelic, D. (2023). Learning provably stabilizing neural controllers for discrete-time stochastic systems. In 21st International Symposium on Automated Technology for Verification and Analysis (Vol. 14215, pp. 357–379). Singapore, Singapore: Springer Nature.https://doi.org/10.1007/978-3-031-45329-8_17 

Trinh, V. G., Benhamou, B., Henzinger, T. A., &; Pastva, S. (2023). Trap spaces of multi-valued networks: Definition, computation, and applications. Bioinformatics. Oxford Academic.https://doi.org/10.1093/bioinformatics/btad262 

Majumdar, R., Mallik, K., Schmuck, A. K., &; Soudjani, S. (2023). Symbolic control for stochastic systems via finite parity games. Nonlinear Analysis: Hybrid Systems. Elsevier.https://doi.org/10.1016/j.nahs.2023.101430 

Pastva, S., &; Henzinger, T. A. (2023). Binary decision diagrams on modern hardware. In Proceedings of the 23rd Conference on Formal Methods in Computer-Aided Design (pp. 122–131). Ames, IA, United States: TU Vienna Academic Press.https://doi.org/10.34727/2023/isbn.978-3-85448-060-0_20 

Zikelic, D., Lechner, M., Henzinger, T. A., &; Chatterjee, K. (2023). Learning control policies for stochastic systems with reach-avoid guarantees. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (Vol. 37, pp. 11926–11935). Washington, DC, United States: Association for the Advancement of Artificial Intelligence.https://doi.org/10.1609/aaai.v37i10.26407