14 citations · 16 across the 4 of their papers we have counts for
5 papers
Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions
Daniela De Canditiis, Italia De Feis, Paola Stolfi
Feature selection in neural networks remains a challenging problem, particularly in the presence of noisy or contaminated data. LassoNet is a recent approach that addresses this is…
Bayesian methods for the identification of model parameters for water transport in porous media
Paola Stolfi, Elia Onofri, Gabriella Bretti
The structure of the nonlinear inverse problem arising from capillarity-driven imbibition in porous media is investigated, considering a degenerate parabolic PDE with compactly sup…
Normal approximation of Random Gaussian Neural Networks
Nicola Apollonio, Daniela De Canditiis, Giovanni Franzina +2
In this paper we provide explicit upper bounds on some distances between the (law of the) output of a random Gaussian NN and (the law of) a random Gaussian vector. Our results conc…
NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification
Paola Stolfi, Andrea Mastropietro, Giuseppe Pasculli +2
Gene-disease associations are fundamental for understanding disease etiology and developing effective interventions and treatments. Identifying genes not yet associated with a dise…
The Sparse Multivariate Method of Simulated Quantiles
Mauro Bernardi, Lea Petrella, Paola Stolfi
In this paper the method of simulated quantiles (MSQ) of Dominicy and Veredas (2013) and Dominick et al. (2013) is extended to a general multivariate framework (MMSQ) and to provid…