2 papers
cs.LG2026
Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees
Shurong Lin, Aleksandra SlavkoviÄ, Deekshith Reddy Bhoomireddy
In the social sciences, small- to medium-scale datasets are common, and linear regression is canonical. In privacy-aware settings, much work has focused on differentially private (…
cs.LG2025
High-Dimensional Privacy-Utility Dynamics of Noisy Stochastic Gradient Descent on Least Squares
Shurong Lin, Eric D. Kolaczyk, Adam Smith +1
The interplay between optimization and privacy has become a central theme in privacy-preserving machine learning. Noisy stochastic gradient descent (SGD) has emerged as a cornersto…