2 citations · 4 across the 9 of their papers we have counts for
10 papers
A Sharp Transition in Data Reconstruction under Differential Privacy
Max Cairney-Leeming, Simone Bombari, Marco Mondelli
Data reconstruction attacks have empirically been successful in recovering training samples from learned models, raising privacy concerns and motivating defenses with guarantees th…
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…
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…
Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features
Simone Bombari, Marco Mondelli
Understanding the reasons behind the exceptional success of transformers requires a better analysis of why attention layers are suitable for NLP tasks. In particular, such tasks re…
How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features
Simone Bombari, Marco Mondelli
Deep learning models are known to overfit and memorize spurious features in the training dataset. While numerous empirical studies have aimed at understanding this phenomenon, a ri…
Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels
Simone Bombari, Shayan Kiyani, Marco Mondelli
Machine learning models are vulnerable to adversarial perturbations, and a thought-provoking paper by Bubeck and Sellke has analyzed this phenomenon through the lens of over-parame…
Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization
Simone Bombari, Mohammad Hossein Amani, Marco Mondelli
The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide memorization, optimization and generalization guarantees in deep neural networks. A line of work has studi…
Sharp asymptotics on the compression of two-layer neural networks
Mohammad Hossein Amani, Simone Bombari, Marco Mondelli +2
In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M<N nodes. More precisely, we consider the setting in which…