| Characterizing audio adversarial examples using temporal dependency Z Yang, B Li, PY Chen, D Song arXiv preprint arXiv:1809.10875, 2018 | 213 | 2018 |
| G-pate: Scalable differentially private data generator via private aggregation of teacher discriminators Y Long, B Wang, Z Yang, B Kailkhura, A Zhang, C Gunter, B Li Advances in Neural Information Processing Systems 34, 2965-2977, 2021 | 75 | 2021 |
| Trs: Transferability reduced ensemble via promoting gradient diversity and model smoothness Z Yang, L Li, X Xu, S Zuo, Q Chen, P Zhou, B Rubinstein, C Zhang, B Li Advances in Neural Information Processing Systems 34, 17642-17655, 2021 | 70 | 2021 |
| On the certified robustness for ensemble models and beyond Z Yang, L Li, X Xu, B Kailkhura, T Xie, B Li arXiv preprint arXiv:2107.10873, 2021 | 58 | 2021 |
| Re-vilm: Retrieval-augmented visual language model for zero and few-shot image captioning Z Yang, W Ping, Z Liu, V Korthikanti, W Nie, DA Huang, L Fan, Z Yu, S Lan, ... arXiv preprint arXiv:2302.04858, 2023 | 30 | 2023 |
| Uncovering the connections between adversarial transferability and knowledge transferability K Liang, JY Zhang, B Wang, Z Yang, S Koyejo, B Li International Conference on Machine Learning, 6577-6587, 2021 | 28* | 2021 |
| GANs for children: A generative data augmentation strategy for children speech recognition P Sheng, Z Yang, Y Qian 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU …, 2019 | 25 | 2019 |
| Nvlm: Open frontier-class multimodal llms W Dai, N Lee, B Wang, Z Yang, Z Liu, J Barker, T Rintamaki, M Shoeybi, ... arXiv preprint arXiv:2409.11402, 2024 | 20 | 2024 |
| Towards mitigating audio adversarial perturbations Z Yang, B Li, PY Chen, D Song | 17 | 2018 |
| Geoecg: Data augmentation via wasserstein geodesic perturbation for robust electrocardiogram prediction J Zhu, J Qiu, Z Yang, D Weber, MA Rosenberg, E Liu, B Li, D Zhao Machine Learning for Healthcare Conference, 172-197, 2022 | 16 | 2022 |
| Data augmentation using conditional generative adversarial networks for robust speech recognition P Sheng, Z Yang, H Hu, T Tan, Y Qian 2018 11th international symposium on Chinese spoken language processing …, 2018 | 14 | 2018 |
| Improving certified robustness via statistical learning with logical reasoning Z Yang, Z Zhao, B Wang, J Zhang, L Li, H Pei, B Karlaš, J Liu, H Guo, ... Advances in Neural Information Processing Systems 35, 34859-34873, 2022 | 13 | 2022 |
| End-to-end robustness for sensing-reasoning machine learning pipelines Z Yang, Z Zhao, H Pei, B Wang, B Karlas, J Liu, H Guo, B Li, C Zhang arXiv preprint arXiv:2003.00120, 2020 | 8 | 2020 |
| Interpolation for robust learning: Data augmentation on geodesics J Zhu, J Qiu, A Guha, Z Yang, XL Nguyen, B Li, D Zhao ArXiv, 2023 | 3 | 2023 |
| Interpolation for robust learning: data augmentation on wasserstein geodesics J Zhu, J Qiu, A Guha, Z Yang, XL Nguyen, B Li, D Zhao International Conference on Machine Learning, 43129-43157, 2023 | 2 | 2023 |
| How to cover up anomalous accesses to electronic health records X Xu, Q Hao, Z Yang, B Li, D Liebovitz, G Wang, CA Gunter 32nd USENIX Security Symposium (USENIX Security 23), 229-246, 2023 | 2 | 2023 |
| Understanding Robustness in Teacher-Student Setting: A New Perspective Z Yang, Z Chen, T Cai, X Chen, B Li, Y Tian arXiv preprint arXiv:2102.13170, 2021 | 2 | 2021 |
| Characterizing adversarial transferability via gradient orthogonality and smoothness Z Yang, L Li, X Xu, S Zuo, Q Chen, B Rubinstein, C Zhang, B Li Proc. Int. Conf. Mach. Learn. Workshop, 2020 | 2 | 2020 |
| Data augmentation via wasserstein geodesic perturbation for robust electrocardiogram prediction J Zhu, J Qiu, Z Yang, M Rosenberg, E Liu, B Li, D Zhao | 1 | |