4 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…
Effective Non-Random Extreme Learning Machine
Daniela De Canditiis, Fabiano Veglianti
The Extreme Learning Machine (ELM) is a growing statistical technique widely applied to regression problems. In essence, ELMs are single-layer neural networks where the hidden laye…
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…
Estimation of delta-contaminated density of the random intensity of Poisson data
Daniela De Canditiis, Marianna Pensky
In the present paper, we constructed an estimator of a delta contaminated mixing density function of the intensity of the Poisson distribution. The estimator is based on…