3 papers
math.ST2026
Precise Asymptotics for Spectral Methods in Mixed Generalized Linear Models
Yihan Zhang, Marco Mondelli, Ramji Venkataramanan
In a mixed generalized linear model, the goal is to learn multiple signals from unlabeled observations: each sample comes from exactly one signal, but it is not known which one. We…
stat.ML2025
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…
cs.LG2025
Average gradient outer product as a mechanism for deep neural collapse
Daniel Beaglehole, Peter SúkenÃk, Marco Mondelli +1
Deep Neural Collapse (DNC) refers to the surprisingly rigid structure of the data representations in the final layers of Deep Neural Networks (DNNs). Though the phenomenon has been…