Publications
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2024
HELFRICH Martin, ANDRIUSHCHENKO Roman, ČEŠKA Milan, KŘETÍNSKÝ Jan, MARTIČEK Štefan and ŠAFRÁNEK David. Abstraction-based segmental simulation of reaction networks using adaptive memoization. BMC Bioinformatics, vol. 25, no. 1, 2024, pp. 1-24. ISSN 1471-2105.
DetailANDRIUSHCHENKO Roman, ČEŠKA Milan, JUNGES Sebastian and MACÁK Filip. Policies Grow on Trees: Model Checking Families of MDPs. In: Proceeding of 22nd International Symposium on Automated Technology for Verification and Analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Cham: Springer Verlag, 2024, p. 13.
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2023
ANDRIUSHCHENKO Roman, BARTOCCI Ezio, ČEŠKA Milan, FRANCESCO Pontiggia and SARAH Sallinger. Deductive Controller Synthesis for Probabilistic Hyperproperties. In: Quantitative Evaluation of SysTems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14287. Cham: Springer Verlag, 2023, pp. 288-306. ISBN 978-3-031-43834-9.
DetailANDRIUSHCHENKO Roman, ALEXANDER Bork, ČEŠKA Milan, JUNGES Sebastian, KATOEN Joost-Pieter and MACÁK Filip. Search and Explore: Symbiotic Policy Synthesis in POMDPs. In: Computer Aided Verification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13966. Cham: Springer Verlag, 2023, pp. 113-135. ISBN 978-3-031-37708-2.
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2022
ANDRIUSHCHENKO Roman, ČEŠKA Milan, MARCIN Vladimír and VOJNAR Tomáš. GPU-Accelerated Synthesis of Probabilistic Programs. In: International Conference on Computer Aided Systems Theory (EUROCAST'22). Lecture Notes in Computer Science. Cham, 2022, pp. 256-266. ISBN 978-3-031-25312-6.
DetailANDRIUSHCHENKO Roman, ČEŠKA Milan, JUNGES Sebastian and KATOEN Joost-Pieter. Inductive Synthesis of Finite-State Controllers for POMDPs. In: Conference on Uncertainty in Artificial Intelligence. Proceedings of Machine Learning Research, vol. 180. Eindhoven: Proceedings of Machine Learning Research, 2022, pp. 85-95. ISSN 2640-3498.
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2021
ABATE Alessandro, ANDRIUSHCHENKO Roman, ČEŠKA Milan and KWIATKOWSKA Marta. Adaptive formal approximations of Markov chains. Performance Evaluation, vol. 148, no. 102207, 2021, pp. 1-23. ISSN 0166-5316.
DetailANDRIUSHCHENKO Roman, ČEŠKA Milan, JUNGES Sebastian and KATOEN Joost-Pieter. Inductive Synthesis for Probabilistic Programs Reaches New Horizons. In: International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS). Lecture Notes in Computer Science. Cham: Springer International Publishing, 2021, pp. 191-209. ISBN 978-3-030-72015-5.
DetailANDRIUSHCHENKO Roman, ČEŠKA Milan, JUNGES Sebastian, KATOEN Joost-Pieter and STUPINSKÝ Šimon. PAYNT: A Tool for Inductive Synthesis of Probabilistic Programs. In: International Conference on Computer Aided Verification (CAV). Lecture Notes in Computer Science, vol. 12759. Cham: Springer Verlag, 2021, pp. 856-869. ISBN 978-3-030-81684-1.
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