no code implementations • 13 Mar 2024 • Francesca Bartolucci, Ernesto de Vito, Lorenzo Rosasco, Stefano Vigogna
Studying the function spaces defined by neural networks helps to understand the corresponding learning models and their inductive bias.
no code implementations • 29 Jun 2023 • Valentina Cammarota, Domenico Marinucci, Michele Salvi, Stefano Vigogna
We prove a Quantitative Functional Central Limit Theorem for one-hidden-layer neural networks with generic activation function.
no code implementations • 20 May 2022 • Vivien Cabannes, Stefano Vigogna
Classification is often the first problem described in introductory machine learning classes.
no code implementations • 3 Feb 2022 • Stefano Vigogna, Giacomo Meanti, Ernesto de Vito, Lorenzo Rosasco
We study the behavior of error bounds for multiclass classification under suitable margin conditions.
no code implementations • 20 Sep 2021 • Francesca Bartolucci, Ernesto de Vito, Lorenzo Rosasco, Stefano Vigogna
Characterizing the function spaces corresponding to neural networks can provide a way to understand their properties.
no code implementations • NeurIPS 2021 • Luigi Carratino, Stefano Vigogna, Daniele Calandriello, Lorenzo Rosasco
We introduce ParK, a new large-scale solver for kernel ridge regression.
no code implementations • 13 Jan 2021 • Wenjing Liao, Mauro Maggioni, Stefano Vigogna
We consider the regression problem of estimating functions on $\mathbb{R}^D$ but supported on a $d$-dimensional manifold $ \mathcal{M} \subset \mathbb{R}^D $ with $ d \ll D $.