| Improving black-box adversarial attacks with a transfer-based prior S Cheng, Y Dong, T Pang, H Su, J Zhu Advances in neural information processing systems 32, 2019 | 262 | 2019 |
| Adversarial vision challenge W Brendel, J Rauber, A Kurakin, N Papernot, B Veliqi, SP Mohanty, ... The NeurIPS'18 Competition: From Machine Learning to Intelligent …, 2020 | 61 | 2020 |
| Query-efficient black-box adversarial attacks guided by a transfer-based prior Y Dong, S Cheng, T Pang, H Su, J Zhu IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (12), 9536 …, 2021 | 34 | 2021 |
| Stochastic gradient hamiltonian monte carlo with variance reduction for bayesian inference Z Li, T Zhang, S Cheng, J Zhu, J Li Machine Learning 108, 1701-1727, 2019 | 26 | 2019 |
| Defense against adversarial attacks via controlling gradient leaking on embedded manifolds Y Li, S Cheng, H Su, J Zhu Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 19 | 2020 |
| On the convergence of prior-guided zeroth-order optimization algorithms S Cheng, G Wu, J Zhu Advances in Neural Information Processing Systems 34, 14620-14631, 2021 | 9 | 2021 |
| A Wasserstein minimum velocity approach to learning unnormalized models Z Wang, S Cheng, L Yueru, J Zhu, B Zhang International Conference on Artificial Intelligence and Statistics, 3728-3738, 2020 | 9 | 2020 |
| Switching Transferable Gradient Directions for Query-Efficient Black-Box Adversarial Attacks C Ma, S Cheng, L Chen, J Zhu, J Yong arXiv preprint arXiv:2009.07191, 2020 | 3 | 2020 |
| Supplementary Material for: Defense Against Adversarial Attacks via Controlling Gradient Leaking on Embedded Manifolds Y Li, S Cheng, H Su, J Zhu | | |
