4 papers
High-Dimensional Private Linear Regression with Optimal Rates
Simone Bombari, Jialei Luo, Inbar Seroussi +1
Differentially private (DP) linear regression has received significant attention in the recent theoretical literature, with several approaches proposed to improve error rates. Our…
A Law of Data Reconstruction for Random Features (and Beyond)
Leonardo Iurada, Simone Bombari, Tatiana Tommasi +1
Large-scale deep learning models are known to memorize parts of the training set. In machine learning theory, memorization is often framed as interpolation or label fitting, and cl…
Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization
Simone Bombari, Marco Mondelli
Learning models have been shown to rely on spurious correlations between non-predictive features and the associated labels in the training data, with negative implications on robus…
Privacy for Free in the Overparameterized Regime
Simone Bombari, Marco Mondelli
Differentially private gradient descent (DP-GD) is a popular algorithm to train deep learning models with provable guarantees on the privacy of the training data. In the last decad…