Recent publications
Book
Abate, A, Giacobbe, M & Roy, D 2021, Learning Probabilistic Termination Proofs. https://doi.org/10.1007/978-3-030-81688-9_1
Giacobbe, M, Henzinger, TA & Lechner, M 2020, How Many Bits Does it Take to Quantize Your Neural Network?. https://doi.org/10.1007/978-3-030-45237-7_5
Frehse, G, Giacobbe, M & Henzinger, TA 2018, Space-time interpolants. https://doi.org/10.1007/978-3-319-96145-3_25
Bogomolov, S, Giacobbe, M, Henzinger, TA & Kong, H 2017, Conic abstractions for hybrid systems. https://doi.org/10.1007/978-3-319-65765-3_7
Bogomolov, S, Frehse, G, Giacobbe, M & Henzinger, TA 2017, Counterexample-guided refinement of template polyhedra. https://doi.org/10.1007/978-3-662-54577-5_34
Giacobbe, M, Guet, CC, Gupta, A, Henzinger, TA, Paixao, T & Petrov, T 2015, Model checking gene regulatory networks. https://doi.org/10.1007/978-3-662-46681-0_47
Biallas, S, Giacobbe, M & Kowalewski, S 2013, Predicate abstraction for programmable logic controllers. https://doi.org/10.1007/978-3-642-41010-9_9
Article
Abate, A, Ahmed, D, Giacobbe, M & Peruffo, A 2021, 'Formal synthesis of Lyapunov neural networks', IEEE Control Systems Letters, vol. 5, no. 3, pp. 773-778. https://doi.org/10.1109/LCSYS.2020.3005328
Giacobbe, M, Guet, CC, Gupta, A, Henzinger, TA, Paixão, T & Petrov, T 2017, 'Model checking the evolution of gene regulatory networks', Acta Informatica. https://doi.org/10.1007/s00236-016-0278-x
Chapter
Alur, R, Giacobbe, M, Henzinger, TA, Larsen, KG & Mikučionis, M 2019, Continuous-time models for system design and analysis. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-91908-9_22
Conference contribution
Abate, A, Ahmed, D, Edwards, A, Giacobbe, M & Peruffo, A 2021, FOSSIL: a software tool for the formal synthesis of lyapunov functions and barrier certificates using neural networks. in HSCC '21: Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control., 24, Association for Computing Machinery (ACM). https://doi.org/10.1145/3447928.3456646
Bacci, E, Giacobbe, M & Parker, D 2021, Verifying reinforcement learning up to infinity. in Z-H Zhou (ed.), Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence: Montreal, 19-27 August 2021. International Joint Conferences on Artificial Intelligence Organization (IJCAI), pp. 2154-2160, 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 21/08/21. https://doi.org/10.24963/ijcai.2021/297
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