| Differentially private fine-tuning of language models D Yu, S Naik, A Backurs, S Gopi, HA Inan, G Kamath, J Kulkarni, YT Lee, ... International Conference on Learning Representations (ICLR-22), 2021 | 360 | 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 | 118 | 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 | 107 | 2021 |
| Availability attacks create shortcuts D Yu, H Zhang, W Chen, J Yin, TY Liu ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-22), 2022 | 77* | 2022 |
| 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 | 64 | 2020 |
| Advancing differential privacy: Where we are now and future directions for real-world deployment R Cummings, D Desfontaines, D Evans, R Geambasu, Y Huang, ... Harvard Data Science Review, 2024 | 60* | 2024 |
| 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 International Conference on Learning Representations (ICLR-23), 2022 | 48 | 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 | 45 | 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 | 35* | 2022 |
| Selective pre-training for private fine-tuning D Yu, S Gopi, J Kulkarni, Z Lin, S Naik, TL Religa, J Yin, H Zhang Transactions on Machine Learning Research (TMLR), 2023 | 26 | 2023 |
| Differentially private synthetic data via foundation model apis 2: Text C Xie, Z Lin, A Backurs, S Gopi, D Yu, HA Inan, H Nori, H Jiang, H Zhang, ... International Conference on Machine Learning (ICML-24), 2024 | 23 | 2024 |
| Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent D Yu, G Kamath, J Kulkarni, J Yin, TY Liu, H Zhang Transactions on Machine Learning Research (TMLR), 2022 | 20 | 2022 |
| Privacy-Preserving Instructions for Aligning Large Language Models D Yu, P Kairouz, S Oh, Z Xu International Conference on Machine Learning (ICML-24), 2024 | 16 | 2024 |
| Improve the Gradient Perturbation Approach for Differentially Private Optimization D Yu, H Zhang, W Chen Privacy Preserving Machine Learning (NeurIPS 2018 Workshop), 0 | 9* | |
| Training Private and Efficient Language Models with Synthetic Data from LLMs D Yu, A Backurs, S Gopi, H Inan, J Kulkarni, Z Lin, C Xie, H Zhang, ... Socially Responsible Language Modelling Research, 2023 | 7 | 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 (AISTATS-23), 2023 | 2 | 2023 |
| On the Stability of Multi-branch Network H Zhang, D Yu, W Chen, TY Liu | | 2020 |
Huishuai ZhangPeking UniversityVerified email at pku.edu.cn