| Differentially private fine-tuning of language models D Yu, S Naik, A Backurs, S Gopi, HA Inan, G Kamath, J Kulkarni, YT Lee, ... arXiv preprint arXiv:2110.06500, 2021 | 87 | 2021 |
| Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning D Yu, H Zhang, W Chen, TY Liu International Conference on Learning Representations (ICLR-21), 2021 | 51 | 2021 |
| Large Scale Private Learning via Low-rank Reparametrization D Yu, H Zhang, W Chen, J Yin, TY Liu International Conference on Machine Learning (ICML-21), 2021 | 38 | 2021 |
| How Does Data Augmentation Affect Privacy in Machine Learning? D Yu, H Zhang, W Chen, J Yin, TY Liu AAAI Conference on Artificial Intelligence (AAAI-21), 2020 | 28 | 2020 |
| Availability attacks create shortcuts D Yu, H Zhang, W Chen, J Yin, TY Liu Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 27* | 2022 |
| Gradient perturbation is underrated for differentially private convex optimization D Yu, H Zhang, W Chen, TY Liu, J Yin International Joint Conference on Artificial Intelligence (IJCAI-20), 2019 | 26 | 2019 |
| Stabilize deep ResNet with a sharp scaling factor H Zhang, D Yu, M Yi, W Chen, TY Liu Machine Learning 111 (9), 3359-3392, 2022 | 22* | 2022 |
| Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping J He, X Li, D Yu, H Zhang, J Kulkarni, YT Lee, A Backurs, N Yu, J Bian arXiv preprint arXiv:2212.01539, 2022 | 6 | 2022 |
| Improve the Gradient Perturbation Approach for Differentially Private Optimization D Yu, H Zhang, W Chen Privacy Preserving Machine Learning (NeurIPS 2018 Workshop), 0 | 6* | |
| Per-instance privacy accounting for differentially private stochastic gradient descent D Yu, G Kamath, J Kulkarni, J Yin, TY Liu, H Zhang arXiv preprint arXiv:2206.02617, 2022 | 4 | 2022 |
| Challenges towards the Next Frontier in Privacy R Cummings, D Desfontaines, D Evans, R Geambasu, M Jagielski, ... arXiv preprint arXiv:2304.06929, 2023 | 3 | 2023 |
| Selective Pre-training for Private Fine-tuning D Yu, S Gopi, J Kulkarni, Z Lin, S Naik, TL Religa, J Yin, H Zhang arXiv preprint arXiv:2305.13865, 2023 | | 2023 |
| Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks H Zhang, D Yu, Y Lu, D He International Conference on Artificial Intelligence and Statistics, 2792-2804, 2023 | | 2023 |
| On the Stability of Multi-branch Network H Zhang, D Yu, W Chen, TY Liu | | |
Huishuai ZhangMicrosoft Research AsiaVerified email at microsoft.com
