Da Yu
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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
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
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
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
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
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
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
Advancing differential privacy: Where we are now and future directions for real-world deployment
R Cummings, D Desfontaines, D Evans, R Geambasu, Y Huang, ...
PubPub 6 (1), 2024
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
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
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
Improve the Gradient Perturbation Approach for Differentially Private Optimization
D Yu, H Zhang, W Chen
Privacy Preserving Machine Learning (NeurIPS 2018 Workshop), 0
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, ...
arXiv preprint arXiv:2403.01749, 2024
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
Privacy-Preserving Instructions for Aligning Large Language Models
D Yu, P Kairouz, S Oh, Z Xu
arXiv preprint arXiv:2402.13659, 2024
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
On the Stability of Multi-branch Network
H Zhang, D Yu, W Chen, TY Liu
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