Aleksandar Bojchevski and Stephan Gunnemann. Certifiable robustness to graph perturbations. In Neural Information Processing Systems, NeurIPS, 2019.
Aleksandar Bojchevski and Stephan Gunnemann. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. In International Conference on Learning Representations, 2018.
Aleksandar Bojchevski, Johannes Gasteiger, and Stephan Gunnemann. Efficient robustness certificates for discrete data: Sparsity-aware randomized smoothing for graphs, images and more. In Proceedings of the 37th International Conference on Machine Learning, pages 1003-1013, 2020.
Shaked Brody, Uri Alon, and Eran Yahav. How attentive are graph attention networks? In International Conference on Learning Representations, 2022.
Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, and Alexey Kurakin. On evaluating adversarial robustness. CoRR, abs/1902.06705, 2019.
Wenda Chu, Linyi Li, and Bo Li. TPC: transformation-specific smoothing for point cloud models. In ICML, volume 162 of Proceedings of Machine Learning Research, pages 4035-4056. PMLR, 2022.
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter. Certified adversarial robustness via randomized smoothing. In International Conference on Machine Learning, pages 1310-1320. PMLR, 2019.
Francesco Croce and Matthias Hein. Sparse and imperceivable adversarial attacks. In ICCV, pages 4723-4731. IEEE, 2019.
Matthias Fey and Jan E. Lenssen. Fast graph representation learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.
Zhidong Gao, Rui Hu, and Yanmin Gong. Certified robustness of graph classification against topology attack with randomized smoothing. In GLOBECOM, pages 1-6. IEEE, 2020.
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. In *ICLR (Poster)*, 2015.
Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, and Stephan Gunnemann. Adversarial training for graph neural networks: Pitfalls, solutions, and new directions. In Advances in Neural Information Processing Systems, NeurIPS, 2023a.
Lukas Gosch, Daniel Sturm, Simon Geisler, and Stephan Gunnemann. Revisiting robustness in graph machine learning. In International Conference on Learning Representations (ICLR), 2023b.
Stephan Gunnemann. Graph neural networks: Adversarial robustness. In Lingfei Wu, Peng Cui, Jian Pei, and Liang Zhao, editors, Graph Neural Networks: Foundations, Frontiers, and Applications, chapter 8, pages 149-176. Springer, 2022.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778. IEEE Computer Society, 2016.
Dan Hendrycks, Nicholas Carlini, John Schulman, and Jacob Steinhardt. Unsolved problems in ML safety. CoRR, abs/2109.13916, 2021.
Miklos Z. Horvath, Mark Niklas Müller, Marc Fischer, and Martin T. Vechev. Boosting randomized smoothing with variance reduced classifiers. In *ICLR*. OpenReview.net, 2022a.
Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, and Martin T. Vechev. (De-)Randomized Smoothing for Decision Stump Ensembles. In Advances in Neural Information Processing Systems, NeurIPS, 2022b.
Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, and Martin T. Vechev. Robust and accurate - compositional architectures for randomized smoothing. CoRR, abs/2204.00487, 2022c.