[16] C. Bucilua, R. Caruana, and A. Niculescu-Mizil, "Model compression," in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'06), 2006, pp. 535-541.
[17] G. Hinton, O. Vinyals, and J. Dean, "Distilling the knowledge in a neural network," arXiv preprint arXiv:1503.02531, 2015.
[18] T. Brázdil, K. Chatterjee, M. Chmelik, V. Forejt, J. Křetínský, M. Kwiatkowska, D. Parker, and M. Ujma, “Verification of markov decision processes using learning algorithms,” in 12th International Symposium on Automated Technology for Verification and Analysis (ATVA'14). Springer, 2014, pp. 98–114.
[19] D. Abel, D. E. Hershkowitz, and M. L. Littman, “Near optimal behavior via approximate state abstraction,” in International Conference on Machine Learning (ICML '16), vol. 48, 2016, pp. 2915–2923.
[20] D. Abel, “A theory of state abstraction for reinforcement learning,” in AAAI Conference on Artificial Intelligence (AAAI'19), vol. 33, no. 01, 2019, pp. 9876–9877.
[21] P. Jin, J. Tian, D. Zhi, X. Wen, and M. Zhang, "Trainify: A CEGAR-driven training and verification framework for safe deep reinforcement learning," in International Conference on Computer Aided Verification (CAV'22). Springer, 2022, pp. 193-218.
[22] J. Tian, D. Zhi, S. Liu, P. Wang, G. Katz, and M. Zhang, "Taming reachability analysis of dnn-controlled systems via abstraction-based training," in 25th International Conference on Verification, Model Checking, and Abstract Interpretation (VMCAI'23). Springer, 2023.
[23] R. Alur, C. Courcoubetis, N. Halbwachs, T. A. Henzinger, P.-H. Ho, X. Nicollin, A. Olivero, J. Sifakis, and S. Yovine, "The algorithmic analysis of hybrid systems," Theoretical Computer Science, vol. 138, no. 1, pp. 3-34, 1995.
[24] X. Chen, E. Ábráham, and S. Sankaranarayanan, "Flow*: An analyzer for non-linear hybrid systems," in 25th International Conference on Computer Aided Verification (CAV'13). Springer, 2013, pp. 258-263.
[25] P. Collins, D. Bresolin, L. Geretti, and T. Villa, "Computing the evolution of hybrid systems using rigorous function calculus," IFAC Proceedings Volumes, vol. 45, no. 9, pp. 284-290, 2012.
[26] M. Althoff, "An introduction to cora 2015." ARCH@ CPSWeek, vol. 34, pp. 120-151, 2015.
[27] X. Chen, "Reachability analysis of non-linear hybrid systems using taylor models," Ph.D. dissertation, Fachgruppe Informatik, RWTH Aachen University, 2015.
[28] C. Huang, J. Fan, W. Li, X. Chen, and Q. Zhu, "Reachnn: Reachability analysis of neural-network controlled systems," ACM Transactions on Embedded Computing Systems, vol. 18, no. 5s, pp. 1-22, 2019.
[29] M. Grzes and D. Kudenko, "Multigrid reinforcement learning with reward shaping," in 18th International Conference Artificial Neural Networks (ICANN'08). Springer, 2008, pp. 357-366.
[30] H. Zhu, Z. Xiong, S. Magill, and S. Jagannathan, "An inductive synthesis framework for verifiable reinforcement learning," in Proceedings of the 40th ACM SIGPLAN conference on Programming Language Design and Implementation (PLDI'19), 2019, pp. 686-701.
[31] Y. Wang and H. Zhu, “Verification-guided programmatic controller synthesis,” in 29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS'23). Springer, 2023, pp. 229–250.
[32] P. Cousot and R. Cousot, "Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints," in Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages (POPL'77), 1977, pp. 238-252.
[33] M. Afzal, A. Asia, A. Chauhan, B. Chimdyalwar, P. Darke, A. Datar, S. Kumar, and R. Venkatesh, “Veriabs: Verification by abstraction and test generation,” in The 34th IEEE/ACM International Conference on Automated Software Engineering (ASE'19). IEEE, 2019, pp. 1138–1141.
[34] K. Heo, H. Oh, and H. Yang, “Resource-aware program analysis via online abstraction coarsening,” in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE'19). IEEE, 2019, pp. 94–104.
[35] Z. Wang, A. Albarghouthi, G. Prakriya, and S. Jha, "Interval universal approximation for neural networks," Proceedings of the ACM on Programming Languages (POPL), vol. 6, no. POPL, pp. 1-29, 2022.
[36] E. Gallestey and P. Hokayem, “Lecture notes in nonlinear systems and control,” 2019.