no code implementations • 27 Apr 2024 • Simone Tonini, Andrea Vandin, Francesca Chiaromonte, Daniele Licari, Fernando Barsacchi
We present a novel, simple and widely applicable semi-supervised procedure for anomaly detection in industrial and IoT environments, SAnD (Simple Anomaly Detection).
1 code implementation • 26 Mar 2023 • Tobia Boschi, Lorenzo Testa, Francesca Chiaromonte, Matthew Reimherr
Functional regression analysis is an established tool for many contemporary scientific applications.
no code implementations • NeurIPS 2021 • Tobia Boschi, Matthew Reimherr, Francesca Chiaromonte
Feature Selection and Functional Data Analysis are two dynamic areas of research, with important applications in the analysis of large and complex data sets.
no code implementations • 19 Apr 2021 • Vincent Pisztora, Yanglan Ou, Xiaolei Huang, Francesca Chiaromonte, Jia Li
In this paper we propose $\epsilon$-Consistent Mixup ($\epsilon$mu).
no code implementations • 10 Feb 2021 • Andrea Vandin, Daniele Giachini, Francesco Lamperti, Francesca Chiaromonte
We propose a novel approach to the statistical analysis of stochastic simulation models and, especially, agent-based models (ABMs).
1 code implementation • 6 Jun 2020 • Tobia Boschi, Matthew Reimherr, Francesca Chiaromonte
Our new algorithm exploits both the sparsity induced by the Elastic Net penalty and the sparsity due to the second order information of the augmented Lagrangian.
no code implementations • 24 Jul 2019 • Jacopo Di Iorio, Francesca Chiaromonte, Marzia A. Cremona
In the last two decades several biclustering methods have been developed as new unsupervised learning techniques to simultaneously cluster rows and columns of a data matrix.
1 code implementation • 7 Aug 2018 • Ana Kenney, Francesca Chiaromonte, Giovanni Felici
Because of continuous advances in mathematical programing, Mix Integer Optimization has become a competitive vis-a-vis popular regularization method for selecting features in regression problems.
Methodology
no code implementations • 13 Aug 2014 • Bing Li, Hongyuan Zha, Francesca Chiaromonte
We propose a novel approach to sufficient dimension reduction in regression, based on estimating contour directions of negligible variation for the response surface.