1 citations · 1 across the 5 of their papers we have counts for
5 papers
Estimation of Linear Functionals in Multilayer Panels under Staggered Adoption
Alberto Bordino, Thomas B. Berrett, Olga Klopp
We study the estimation of bilinear forms from noisy, partially observed multilayer data. The signal follows a Tucker2 model, with shared unit and time factors across tensor layers…
Nonparametric inference for ratios of densities via uniformly valid and powerful permutation tests
Alberto Bordino, Thomas B. Berrett
We propose the density ratio permutation test, a hypothesis test that assesses whether the ratio between two densities is proportional to a known function based on independent samp…
Tests of Missing Completely At Random based on sample covariance matrices
Alberto Bordino, Thomas B. Berrett
We study the problem of testing whether the missing values of a potentially high-dimensional dataset are Missing Completely at Random (MCAR). We relax the problem of testing MCAR t…
Infinitely wide limits for deep Stable neural networks: sub-linear, linear and super-linear activation functions
Alberto Bordino, Stefano Favaro, Sandra Fortini
There is a growing literature on the study of large-width properties of deep Gaussian neural networks (NNs), i.e. deep NNs with Gaussian-distributed parameters or weights, and Gaus…
Non-asymptotic approximations of Gaussian neural networks via second-order Poincaré inequalities
Alberto Bordino, Stefano Favaro, Sandra Fortini
There is a recent and growing literature on large-width asymptotic and non-asymptotic properties of deep Gaussian neural networks (NNs), namely NNs with weights initialized as Gaus…