2 citations · 4 across the 9 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…