Meng Ding

I am currently a Ph.D. student advised by Prof. Jinhui Xu and Prof. Kaiyi Ji at the State University of New York at Buffalo(UB). I am also very fortunate to closely work with Prof. Di Wang. Previously, I obtained my Bachelor's degree in Mathematics from China Agricultural University.

I am interested in broad areas of data privacy and data memorization. My recent work focuses on privacy-preserving optimization/statistics and understanding model behaviors in (private) deep learning.

Pulications

* indicates equal contribution, † indicates alphabetical order.

Preprints

Not right now, but one day.
Understanding and Mitigating Cross-lingual Privacy Leakage via Language-specific and Universal Privacy Neurons
Wenshuo Dong, Qingsong Yang, Shu Yang, Lijie Hu, Meng Ding, Wanyu Lin, Tianhang Zheng, Di Wang
Towards User-level Private Reinforcement Learning with Human Feedback
Jiaming Zhang, Mingxi Lei, Meng Ding, Mengdi Li, Zihang Xiang, Difei Xu, Jinhui Xu, Di Wang.
Understanding Fine-tuning in Approximate Unlearning: A Theoretical Perspective
Meng Ding, Rohan Sharma, Changyou Chen, Jinhui Xu, Kaiyi Ji.
Evaluating Data Influence in Meta Learning
Chenyang Ren, Huanyi Xie, Shu Yang, Meng Ding, Lijie Hu, Di Wang.

Selected Papers

Differentially Private Sparse Linear Regression with Heavy-tailed Responses
Xizhi Tian, Meng Ding, Youming Tao, Zihang Xiang, Di Wang.
ECML 2025;
Understanding Private Learning From Feature Perspective
Meng Ding, Mingxi Lei, Shaopeng Fu, Shaowei Wang, Di Wang, Jinhui Xu.
TPDP 2025;
Nearly Optimal Differentially Private ReLU Regression
Meng Ding, Mingxi Lei, Shaowei Wang, Tianhang Zheng, Di Wang, Jinhui Xu.
UAI 2025;
TTVD: Towards a Geometric Framework for Test-Time Adaptation Based on Voronoi Diagram.
Mingxi Lei, Chunwei Ma, Meng Ding, Yufan Zhou, Ziyun Huang, Jinhui Xu.
ICLR 2025;
Improved Rates of Differentially Private Nonconvex-Strongly-Concave Minimax Optimization
Ruijia Zhang*, Mingxi Lei*, Meng Ding, Zihang Xiang, Jinhui Xu, Di Wang.
AAAI 2024;
Revisiting Differentially Private ReLU Regression
Meng Ding, Mingxi Lei, Liyang Zhu, Shaowei Wang, Di Wang, Jinhui Xu.
NeurIPS 2024;
Truthful High Dimensional Sparse Linear Regression
Liyang Zhu, Amina Manseur, Meng Ding, Jinyan Liu, Jinhui Xu, Di Wang.
NeurIPS 2024;
Fair Single Index Model
Yidong Wang*,Meng Ding*, Jinhui Xu, Di Wang.
TKDD;
Understanding Forgetting in Continual Learning with Linear Regression
Meng Ding, Kaiyi Ji, Di Wang, Jinhui Xu.
ICML 2024;
Improved Analysis of Sparse Linear Regression in Local Differential Privacy Model
Liyang Zhu*, Meng Ding*, Jinhui Xu, Di Wang.
ICLR 2024;

Services

Conference Reviewer
ICML (2023 - ), NeurIPS (2024 - ), ICLR (2025 - ), AISTATS (2024 - ),