About Me

I'm Da Yu, a PhD student at Sun Yat-sen University since 2019. I'm very lucky to be advised by Prof. Jian Yin and Prof. Tie-Yan Liu. I'm also very lucky to have the opportunity to work with Dr. Huishuai Zhang. I received my B.S. degree of Computer Science from Sun Yat-sen University in 2019.

Currently, I am a research intern at Microsoft Research Asia. I'm interested in (differentially) private machine learning. I'm also interested in understanding the difference between deep learning and classic machine learning through the lens of differential privacy. My research is supported by MSRA Fellowship.

I'm a reviewer of ICML, NeurIPS, and ICLR. I'm a top reviewer of ICML 2022 and NeurIPS 2022.

Email: yuda3@mail2.sysu.edu.cn.

Publications

Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping

Jiyan He, Xuechen Li, Da Yu, Huishuai Zhang, Janardhan Kulkarni, Yin Tat Lee, Arturs Backurs, Nenghai Yu, Jiang Bian.

International Conference on Learning Representations (ICLR), 2023.

Adversarial Noises Are Linearly Separable for (Nearly) Random Neural Networks

Huishuai Zhang, Da Yu, Yiping Lu, Di He.

Artificial Intelligence and Statistics (AISTATS), 2023.

Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent

Da Yu, Gautam Kamath*, Janardhan Kulkarni*, Tie-Yan Liu*, Jian Yin*, Huishuai Zhang* (* denotes alphabetical order).

Preprint, 2022.

Availability Attacks Create Shortcuts

Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu.

SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Research Track, 2022.

Differentially Private Fine-tuning of Language Models

Da Yu, Saurabh Naik, Arturs Backurs*, Sivakanth Gopi*, Huseyin A. Inan*, Gautam Kamath*, Janardhan Kulkarni*, Yin Tat Lee*, Andre Manoel*, Lukas Wutschitz*, Sergey Yekhanin*, Huishuai Zhang* (* denotes alphabetical order).

International Conference on Learning Representations (ICLR), 2022.

Large Scale Private Learning via Low-rank Reparametrization

Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu.

International Conference on Machine Learning (ICML), 2021.

Do not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning

Da Yu*, Huishuai Zhang*, Wei Chen, Tie-Yan Liu.

International Conference on Learning Representations (ICLR), 2021.

How Does Data Augmentation Affect Privacy in Machine Learning?

Da Yu, Huishuai Zhang, Wei Chen, Jian Yin, Tie-Yan Liu.

AAAI Conference on Artificial Intelligence (AAAI), 2021.

Gradient Perturbation is Underrated for Differentially Private Convex Optimization

Da Yu*, Huishuai Zhang*, Wei Chen, Jian Yin, Tie-Yan Liu.

International Joint Conference on Artificial Intelligence (IJCAI), 2020.

Stabilize deep ResNet with a sharp scaling factor τ

Huishuai Zhang, Da Yu, Mingyang Yi, Wei Chen, Tie-Yan Liu

Machine Learning, 2022.

Improve the Gradient Perturbation Approach for Differentially Private Optimization

Da Yu, Huishuai Zhang, Wei Chen.

Privacy Preserving Machine Learning, NeurIPS Workshop, 2018.