| Finding Mixed Nash Equilibria of Generative Adversarial Networks YP Hsieh, C Liu, V Cevher International Conference on Machine Learning (ICML) 2019, 2018 | 77 | 2018 |
| On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them C Liu, M Salzmann, T Lin, R Tomioka, S Süsstrunk Neural Information Processing Systems (NeurIPS) 2020, 2020 | 74 | 2020 |
| On Certifying Non-uniform Bound against Adversarial Attacks C Liu, R Tomioka, V Cevher International Conference on Machine Learning (ICML) 2019, 2019 | 22 | 2019 |
| Consistent 3D Rendering in Medical Imaging C Liu, S Miao, K Petkov, S Sudarsky, D Yu, T Mansi EP Patent EP3,373,245, 2018 | 15* | 2018 |
| Fast adversarial training with adaptive step size Z Huang, Y Fan, C Liu, W Zhang, Y Zhang, M Salzmann, S Süsstrunk, ... IEEE Transactions on Image Processing, 2023 | 11 | 2023 |
| Training Provably Robust Models by Polyhedral Envelope Regularization C Liu, M Salzmann, S Süsstrunk IEEE Transactions on Neural Networks and Learning Systems, 2021 | 10 | 2021 |
| Consistent 3D rendering in medical imaging K Petkov, C Liu, S Miao, S Sudarsky, D Yu, T Mansi US Patent 10,957,098, 2021 | 9 | 2021 |
| Towards Stable and Efficient Adversarial Training against Bounded Adversarial Attacks Y Jiang*, C Liu*, Z Huang, M Salzmann, S Süsstrunk 40th International Conference on Machine Learning (ICML) 2023, 2023 | 6 | 2023 |
| On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training C Liu, Z Huang, M Salzmann, T Zhang, S Süsstrunk arXiv preprint arXiv:2112.07324, 2021 | 5 | 2021 |
| Robust Binary Models by Pruning Randomly-initialized Networks C Liu*, Z Zhao*, S Süsstrunk, M Salzmann Neural Information Processing Systems (NeurIPS) 2022, 2022 | 2 | 2022 |
| Improving Adversarial Defense with Self-supervised Test-time Fine-tuning Z Huang, C Liu, M Salzmann, S Süsstrunk, T Zhang | 2 | 2021 |
| Towards Mitigating Architecture Overfitting in Dataset Distillation X Zhong, C Liu arXiv preprint arXiv:2309.04195, 2023 | 1 | 2023 |
| Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks. C Liu EPFL, 2022 | | 2022 |
Sabine SüsstrunkProfessor, Images and Visual Representation Lab, EPFLVerified email at epfl.ch
