Published Works

  1. Bortolussi, L., Cairoli, F., & Paoletti, N. (2023). Conformal Quantitative Predictive Monitoring of STL Requirements for Stochastic Processes. 26th ACM International Conference on Hybrid Systems: Computation and Control (HSCC 2023), to Appear.
  2. Chen, H., Lin, S., Smolka, S. A., & Paoletti, N. (2023). An STL-based Approach to Resilient Control for Cyber-Physical Systems. 26th ACM International Conference on Hybrid Systems: Computation and Control (HSCC 2023), to Appear.
  3. Krish, V., Paoletti, N., Smolka, S. A., & Rahmati, A. (2023). Synthesizing Pareto-Optimal Signal-Injection Attacks on ICDs. IEEE Access, 11, 4992–5003. https://doi.org/10.1109/ACCESS.2022.3233010.
  4. Cairoli, F., Paoletti, N., & Bortolussi, L. (2022). Neural Predictive Monitoring for Collective Adaptive Systems. ISoLA 2022 Symposium, 13703, 30–46. https://doi.org/10.1007/978-3-031-19759-8_3
  5. Chen, H., Lin, S., Smolka, S. A., & Paoletti, N. (2022). An STL-Based Formulation of Resilience in Cyber-Physical Systems. FORMATS 2022, 13465, 117–135. https://doi.org/10.1007/978-3-031-15839-1_7
  6. Bagga, P., Paoletti, N., & Stathis, K. (2022). Deep Learnable Strategy Templates for Multi-Issue Bilateral Negotiation. 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022, 1533–1535. https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1533.pdf
  7. Chen, H., Paoletti, N., Smolka, S. A., & Lin, S. (2021). MPC-guided Imitation Learning of Bayesian Neural Network Policies for the Artificial Pancreas. 60th IEEE Conference on Decision and Control (CDC 2021), 2525–2532. https://doi.org/10.1109/CDC45484.2021.9683240
  8. Bagga, P., Paoletti, N., Alrayes, B., & Stathis, K. (2021). ANEGMA: an automated negotiation model for e-markets. Autonomous Agents and Multi-Agent Systems, 35(2), 27. https://doi.org/10.1007/s10458-021-09513-x
  9. Wicker, M., Laurenti, L., Patane, A., Paoletti, N., Abate, A., & Kwiatkowska, M. (2021). Certification of Iterative Predictions in Bayesian Neural Networks. Uncertainty in Artificial Intelligence (UAI 2021), 161, 1713–1723. https://proceedings.mlr.press/v161/wicker21a.html
  10. Cairoli, F., Bortolussi, L., & Paoletti, N. (2021). Neural Predictive Monitoring Under Partial Observability. Runtime Verification, 12974, 121–141. https://doi.org/10.1007/978-3-030-88494-9_7
  11. LaMalfa, E., Zbrzezny, A., Michelmore, R., Paoletti, N., & Kwiatkowska, M. (2021). On Guaranteed Optimal Robust Explanations for NLP Models. International Joint Conference on Artificial Intelligence (IJCAI 2021), 2658–2665. https://doi.org/10.24963/ijcai.2021/366
  12. Bortolussi, L., Cairoli, F., Paoletti, N., Smolka, S. A., & Stoller, S. D. (2021). Neural Predictive Monitoring and a Comparison of Frequentist and Bayesian Approaches. International Journal on Software Tools for Technology Transfer, to Appear, 23(4), 615–640. https://doi.org/10.1007/s10009-021-00623-1
  13. Bagga, P., Paoletti, N., & Stathis, K. (2021). Pareto Bid Estimation for Multi-Issue Bilateral Negotiation under User Preference Uncertainty. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2021), 1–6. https://doi.org/10.1109/FUZZ45933.2021.9494429