| Connecting certified and adversarial training Y Mao, M Müller, M Fischer, M Vechev Advances in Neural Information Processing Systems 36, 73422-73440, 2023 | 25 | 2023 |
| " Is your explanation stable?" A Robustness Evaluation Framework for Feature Attribution Y Gan, Y Mao, X Zhang, S Ji, Y Pu, M Han, J Yin, T Wang Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications …, 2022 | 16 | 2022 |
| Transfer attacks revisited: A large-scale empirical study in real computer vision settings Y Mao, C Fu, S Wang, S Ji, X Zhang, Z Liu, J Zhou, AX Liu, R Beyah, ... 2022 IEEE Symposium on Security and Privacy (SP), 1423-1439, 2022 | 16 | 2022 |
| Understanding certified training with interval bound propagation Y Mao, MN Müller, M Fischer, M Vechev arXiv preprint arXiv:2306.10426, 2023 | 13 | 2023 |
| Expressivity of reLU-networks under convex relaxations M Baader, MN Müller, Y Mao, M Vechev arXiv preprint arXiv:2311.04015, 2023 | 6 | 2023 |
| Ctbench: A library and benchmark for certified training Y Mao, S Balauca, M Vechev arXiv preprint arXiv:2406.04848, 2024 | 4 | 2024 |
| Multi-Neuron Unleashes Expressivity of ReLU Networks Under Convex Relaxation Y Mao, Y Zhang, M Vechev arXiv preprint arXiv:2410.06816, 2024 | | 2024 |
| Average Certified Radius is a Poor Metric for Randomized Smoothing C Sun, Y Mao, MN Müller, M Vechev arXiv preprint arXiv:2410.06895, 2024 | | 2024 |
| Transfer Learning Assisted Fast Design Migration Over Technology Nodes: A Study on Transformer Matching Network C Chu, Y Mao, H Wang 2024 IEEE/MTT-S International Microwave Symposium-IMS 2024, 188-191, 2024 | | 2024 |
| Gaussian Loss Smoothing Enables Certified Training with Tight Convex Relaxations S Balauca, MN Müller, Y Mao, M Baader, M Fischer, M Vechev arXiv preprint arXiv:2403.07095, 2024 | | 2024 |
| Overcoming the Paradox of Certified Training with Gaussian Smoothing S Balauca, MN Müller, Y Mao, M Baader, M Fischer, M Vechev arXiv e-prints, arXiv: 2403.07095, 2024 | | 2024 |
