activity
20192021
most citedMultiscale regression on unknown manifolds

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

stat.ML2021

Understanding neural networks with reproducing kernel Banach spaces

Francesca Bartolucci, Ernesto De Vito, Lorenzo Rosasco +1

Characterizing the function spaces corresponding to neural networks can provide a way to understand their properties. In this paper we discuss how the theory of reproducing kernel…

stat.ML20212 cited

Multiscale regression on unknown manifolds

Wenjing Liao, Mauro Maggioni, Stefano Vigogna

We consider the regression problem of estimating functions on but supported on a -dimensional manifold with . Draw…

math.FA2020

Construction and Monte Carlo estimation of wavelet frames generated by a reproducing kernel

Ernesto De Vito, Zeljko Kereta, Valeriya Naumova +2

We introduce a construction of multiscale tight frames on general domains. The frame elements are obtained by spectral filtering of the integral operator associated with a reproduc…

math.ST2020

Estimating multi-index models with response-conditional least squares

Timo Klock, Alessandro Lanteri, Stefano Vigogna

The multi-index model is a simple yet powerful high-dimensional regression model which circumvents the curse of dimensionality assuming for s…

math.FA2019

Monte Carlo wavelets: a randomized approach to frame discretization

Zeljko Kereta, Stefano Vigogna, Valeriya Naumova +2

In this paper we propose and study a family of continuous wavelets on general domains, and a corresponding stochastic discretization that we call Monte Carlo wavelets. First, using…