Learning adaptive multiscale approximations to data and functions near low-dimensional sets. - Wenjing Liao; Mauro Maggioni; Stefano Vigogna (ITW, 2016)

ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions. - Luigi Carratino; Stefano Vigogna; Daniele Calandriello; Lorenzo Rosasco (NeurIPS, 2021)

Multiscale regression on unknown manifolds. - Wenjing Liao; Mauro Maggioni; Stefano Vigogna (CoRR, 2021)

Understanding neural networks with reproducing kernel Banach spaces. - Francesca Bartolucci; Ernesto De Vito; Lorenzo Rosasco; Stefano Vigogna (CoRR, 2021)

Multiclass learning with margin: exponential rates with no bias-variance trade-off. - Stefano Vigogna; Giacomo Meanti; Ernesto De Vito; Lorenzo Rosasco (ICML, 2022)

A Case of Exponential Convergence Rates for SVM. - Vivien Cabannes; Stefano Vigogna (AISTATS, 2023)

How many samples are needed to leverage smoothness? - Vivien Cabannes; Stefano Vigogna (NeurIPS, 2023)

Neural reproducing kernel Banach spaces and representer theorems for deep networks. - Francesca Bartolucci; Ernesto De Vito; Lorenzo Rosasco; Stefano Vigogna (CoRR, 2024)

Spectral complexity of deep neural networks. - Simmaco Di Lillo; Domenico Marinucci; Michele Salvi; Stefano Vigogna (CoRR, 2024)

A Lipschitz spaces view of infinitely wide shallow neural networks. - Francesca Bartolucci; Marcello Carioni; José A. Iglesias 002; Yury Korolev; Emanuele Naldi; Stefano Vigogna (CoRR, 2024)

Fractal and Regular Geometry of Deep Neural Networks. - Simmaco Di Lillo; Domenico Marinucci; Michele Salvi; Stefano Vigogna (CoRR, 2025)