Leonard Berrada, Sumanth Dathathri, Krishnamurthy Dvijotham, Robert Stanforth, Rudy R Bunel, Jonathan Uesato, Sven Gowal, and M Pawan Kumar. Make sure you're unsure: A framework for verifying probabilistic specifications. Advances in Neural Information Processing Systems, 34: 11136-11147, 2021.
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe. Convex optimization. Cambridge University Press, 2004.
Christopher Brix. Vnn-comp 2023 benchmarks. https://github.com/ChristopherBrix/vnncomp2023_benchmarks, 2023.
Christopher Brix, Mark Niklas Müller, Stanley Bak, Taylor T. Johnson, and Changliu Liu. First three years of the international verification of neural networks competition (vnn-comp), 2023. URL https://arxiv.org/abs/2301.05815.
Rudy Bunel, P Mudigonda, Ilker Turkaslan, P Torr, Jingyue Lu, and Pushmeet Kohli. Branch and bound for piecewise linear neural network verification. Journal of Machine Learning Research, 21 (2020), 2020.
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, and Somesh Jha. Atom: Robustifying out-of-distribution detection using outlier mining. In Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2021.
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, and Somesh Jha. Robust out-of-distribution detection for neural networks. In The AAAI-22 Workshop on Adversarial Machine Learning and Beyond, 2022. URL https://openreview.net/forum?id=WMIoZ7O_DPz.
Dimitar Iliev Dimitrov, Gagandeep Singh, Timon Gehr, and Martin Vechev. Provably robust adversarial examples. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=UMfhoMtIaP5.
Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy A Mann, and Pushmeet Kohli. A dual approach to scalable verification of deep networks. In UAI, volume 1, page 3, 2018.
Ruediger Ehlers. Formal verification of piece-wise linear feed-forward neural networks. In International Symposium on Automated Technology for Verification and Analysis, pages 269-286. Springer, 2017.
Michael Everett, Golnaz Habibi, Chuangchuang Sun, and Jonathan P How. Reachability analysis of neural feedback loops. IEEE Access, 9:163938-163953, 2021.
Michael Everett, Rudy Bunel, and Shayegan Omidshafiei. Drip: Domain refinement iteration with polytopes for backward reachability analysis of neural feedback loops. arXiv preprint arXiv:2212.04646, 2022.
Timon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov, Swarat Chaudhuri, and Martin Vechev. Ai2: Safety and robustness certification of neural networks with abstract interpretation. In 2018 IEEE Symposium on Security and Privacy (SP), pages 3-18, 2018. doi: 10.1109/SP.2018.00058.
Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, and Pushmeet Kohli. On the effectiveness of interval bound propagation for training verifiably robust models. arXiv preprint arXiv:1810.12715, 2018.
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich. Deep anomaly detection with outlier exposure. arXiv preprint arXiv:1812.04606, 2018.
Haimin Hu, Mahyar Fazlyab, Manfred Morari, and George J. Pappas. Reach-sdp: Reachability analysis of closed-loop systems with neural network controllers via semidefinite programming. In 2020 59th IEEE Conference on Decision and Control (CDC), pages 5929-5934, 2020. doi: 10.1109/CDC42340.2020.9304296.