7 papers
Understanding the Impact of Differentially Private Training on Memorization of Long-Tailed Data
Jiaming Zhang, Huanyi Xie, Meng Ding +3
Recent research shows that modern deep learning models achieve high predictive accuracy partly by memorizing individual training samples. Such memorization raises serious privacy c…
Understanding Private Learning From Feature Perspective
Meng Ding, Mingxi Lei, Shaopeng Fu +3
Differentially private Stochastic Gradient Descent (DP-SGD) has become integral to privacy-preserving machine learning, ensuring robust privacy guarantees in sensitive domains. Des…
Beyond Ordinary Lipschitz Constraints: Differentially Private Stochastic Optimization with Tsybakov Noise Condition
Difei Xu, Meng Ding, Zihang Xiang +2
We study Stochastic Convex Optimization in the Differential Privacy model (DP-SCO). Unlike previous studies, here we assume the population risk function satisfies the Tsybakov Nois…
Differentially Private Sparse Linear Regression with Heavy-tailed Responses
Xizhi Tian, Meng Ding, Touming Tao +2
As a fundamental problem in machine learning and differential privacy (DP), DP linear regression has been extensively studied. However, most existing methods focus primarily on eit…
Improved Rates of Differentially Private Nonconvex-Strongly-Concave Minimax Optimization
Ruijia Zhang, Mingxi Lei, Meng Ding +3
In this paper, we study the problem of (finite sum) minimax optimization in the Differential Privacy (DP) model. Unlike most of the previous studies on the (strongly) convex-concav…
Nearly Optimal Differentially Private ReLU Regression
Meng Ding, Mingxi Lei, Shaowei Wang +3
In this paper, we investigate one of the most fundamental nonconvex learning problems, ReLU regression, in the Differential Privacy (DP) model. Previous studies on private ReLU reg…