Better Private Linear Regression Through Better Private Feature Selection

Travis Dick, Jennifer Gillenwater, Matthew Joseph

Existing work on differentially private linear regression typically assumes that end users can precisely set data bounds or algorithmic hyperparameters. End users often struggle to…