[link] Bortolussi L, Doz R., Nenzi L., Randone F., Silvetti S., Tribastone M. (2026). P^3MC: Towards Probabilistic Programming Probabilistic Model Checking for Linear Temporal Logic. To apper in International Symposium on Leveraging Applications of Formal Methods, Verification, and Validation (ISOLA 2026).
[link] Doz R., Randone F., Bortolussi L., Medvet E. (2026). Inferring Structural Causal Models from Data with Grammatical Evolution. To appear in 19th International Conference on Parallel Problem Solving from Nature (PPSN).
[link] Randone, F., Doz, R., Tribastone, M., Bortolussi, L. (2026). DeGAS: Gradient-Based Optimization of Probabilistic Programs without Sampling. In Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2026. Lecture Notes in Computer Science, vol 16505. Springer, Cham.
[link] Batz, K., Katoen, J. P., Randone, F., & Winkler, T. (2025). Foundations for Deductive Verification of Continuous Probabilistic Programs: From Lebesgue to Riemann and Back. In Proceedings of the ACM on Programming Languages, 9(OOPSLA1), 421-448.
[link] Doz, R., Randone, F., Medvet, E., & Bortolussi, L. (2025, July). Evolutionary Synthesis of Probabilistic Programs. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 999-1007).
[link] Rønneberg, R. C., Randone, F., Pardo, R., & Wąsowski, A. (2025). Quantifying Privacy Risk with Gaussian Mixtures: RC Rønneberg, F. Randone, R. Pardo, A. Wąsowski. Software and Systems Modeling, 1-22.
[link] Randone, F., Bortolussi, L., Incerto, E., & Tribastone, M. (2024). Inference of Probabilistic Programs with Moment-Matching Gaussian Mixtures. Proceedings of the ACM on Programming Languages, 8(POPL), 1882-1912.
[link] Randone, F., Doz, R., Cairoli, F., & Bortolussi, L. (2024, October). Towards a probabilistic programming approach to analyse collective adaptive systems. In International Symposium on Leveraging Applications of Formal Methods (pp. 168-185). Cham: Springer Nature Switzerland.
[link] Schröer, P., Randone, F., Pardo, R., & Wa̧sowski, A. (2024). Symbolic Quantitative Information Flow for Probabilistic Programs. In Principles of Verification: Cycling the Probabilistic Landscape: Essays Dedicated to Joost-Pieter Katoen on the Occasion of His 60th Birthday, Part I (pp. 128-154). Cham: Springer Nature Switzerland.
[link] Randone, F., Bortolussi, L., & Tribastone, M. (2022, September). Jump Longer to Jump Less: Improving Dynamic Boundary Projection with h-Scaling. In International Conference on Quantitative Evaluation of Systems (pp. 150-170). Cham: Springer International Publishing.
[link] Randone, F., Bortolussi, L., & Tribastone, M. (2021). Refining mean-field approximations by dynamic state truncation. Proceedings of the ACM on Measurement and Analysis of Computing Systems, 5(2), 1-30.
[link] Virgolin, M., De Lorenzo, A., Randone, F., Medvet, E., & Wahde, M. (2021, July). Model learning with personalized interpretability estimation (ML-PIE). In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1355-1364).
[link] Virgolin, M., De Lorenzo, A., Medvet, E., & Randone, F. (2020). Learning a formula of interpretability to learn interpretable formulas. In Parallel Problem Solving from Nature–PPSN XVI: 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part II 16 (pp. 79-93). Springer International Publishing.