26 citations · 137 across the 41 of their papers we have counts for
6 papers · 1 filter
Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model
Peter Súkeník, Marco Mondelli, Christoph Lampert
Neural collapse (NC) refers to the surprising structure of the last layer of deep neural networks in the terminal phase of gradient descent training. Recently, an increasing amount…
Improved Convergence of Score-Based Diffusion Models via Prediction-Correction
Francesco Pedrotti, Jan Maas, Marco Mondelli
Score-based generative models (SGMs) are powerful tools to sample from complex data distributions. Their underlying idea is to (i) run a forward process for time by adding no…
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…
Concentration without Independence via Information Measures
Amedeo Roberto Esposito, Marco Mondelli
We propose a novel approach to concentration for non-independent random variables. The main idea is to ``pretend'' that the random variables are independent and pay a multiplicativ…
Mismatched estimation of non-symmetric rank-one matrices corrupted by structured noise
Teng Fu, YuHao Liu, Jean Barbier +3
We study the performance of a Bayesian statistician who estimates a rank-one signal corrupted by non-symmetric rotationally invariant noise with a generic distribution of singular…
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…