Mimic Visiting Styles by Using a Statistical Approach in a Cultural Event Case Study. - Salvatore Cuomo; Pasquale De Michele; Monica Pragliola; Gerardo Severino (EUSPN/ICTH, 2016)

Computational issues and numerical experiments for Linear Multistep Method Particle Filtering. - Daniela Calvetti; Salvatore Cuomo; Monica Pragliola; Erkki Somersalo; Gerardo Toraldo (CoRR, 2016)

A computational scheme to predict dynamics in IoT systems by using particle filter. - Salvatore Cuomo; Pasquale De Michele; Monica Pragliola (Concurr. Comput. Pract. Exp., 2017)

Space-variant generalised Gaussian regularisation for image restoration. - Alessandro Lanza; Serena Morigi; Monica Pragliola; Fiorella Sgallari (Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2019)

Space-variant Generalized Gaussian Regularization for Image Restoration. - Alessandro Lanza; Serena Morigi; Monica Pragliola; Fiorella Sgallari (CoRR, 2019)

Space-adaptive anisotropic bivariate Laplacian regularization for image restoration. - Luca Calatroni; Alessandro Lanza; Monica Pragliola; Fiorella Sgallari (CoRR, 2019)

A Flexible Space-Variant Anisotropic Regularization for Image Restoration with Automated Parameter Selection. - Luca Calatroni; Alessandro Lanza; Monica Pragliola; Fiorella Sgallari (SIAM J. Imaging Sci., 2019)

Overcomplete representation in a hierarchical Bayesian framework. - Monica Pragliola; Daniela Calvetti; Erkki Somersalo (CoRR, 2020)

Sparsity Promoting Hybrid Solvers for Hierarchical Bayesian Inverse Problems. - Daniela Calvetti; Monica Pragliola; Erkki Somersalo (SIAM J. Sci. Comput., 2020)

Residual Whiteness Principle for Automatic Parameter Selection in ℓ 2-ℓ 2 Image Super-Resolution Problems. - Monica Pragliola; Luca Calatroni; Alessandro Lanza; Fiorella Sgallari (SSVM, 2021)

On and beyond Total Variation regularisation in imaging: the role of space variance. - Monica Pragliola; Luca Calatroni; Alessandro Lanza; Fiorella Sgallari (CoRR, 2021)

ADMM-based residual whiteness principle for automatic parameter selection in super-resolution problems. - Monica Pragliola; Luca Calatroni; Alessandro Lanza; Fiorella Sgallari (CoRR, 2021)

Automatic parameter selection for the TGV regularizer in image restoration under Poisson noise. - Daniela di Serafino; Monica Pragliola (CoRR, 2022)

Whiteness-based parameter selection for Poisson data in variational image processing. - Francesca Bevilacqua; Alessandro Lanza; Monica Pragliola; Fiorella Sgallari (CoRR, 2022)

Nearly Exact Discrepancy Principle for Low-Count Poisson Image Restoration. - Francesca Bevilacqua; Alessandro Lanza; Monica Pragliola; Fiorella Sgallari (J. Imaging, 2022)

Bilevel learning of regularization models and their discretization for image deblurring and super-resolution. - Tatiana A. Bubba; Luca Calatroni; Ambra Catozzi; Serena Crisci; Thomas Pock; Monica Pragliola; Siiri Rautio; Danilo Riccio; Andrea Sebastiani (CoRR, 2023)

ADMM-Based Residual Whiteness Principle for Automatic Parameter Selection in Single Image Super-Resolution Problems. - Monica Pragliola; Luca Calatroni; Alessandro Lanza; Fiorella Sgallari (J. Math. Imaging Vis., 2023)

On and Beyond Total Variation Regularization in Imaging: The Role of Space Variance. - Monica Pragliola; Luca Calatroni; Alessandro Lanza; Fiorella Sgallari (SIAM Rev., 2023)

Whiteness-Based Bilevel Learning of Regularization Parameters in Imaging. - Carlo Santambrogio; Monica Pragliola; Alessandro Lanza; Marco Donatelli; Luca Calatroni (EUSIPCO, 2024)

An efficient hierarchical Bayesian method for the Kuopio tomography challenge 2023. - Monica Pragliola; Daniela Calvetti; Erkki Somersalo (CoRR, 2024)

Insights on the different convergences in Extreme Learning Machine. - Davide Elia De Falco; Francesco Calabrò; Monica Pragliola (Neurocomputing, 2024)

Whiteness-Based Bilevel Estimation of Weighted TV Parameter Maps for Image Denoising. - Monica Pragliola; Luca Calatroni; Alessandro Lanza (SSVM (1), 2025)

Bilevel robust optimization approach for multi-period sparse portfolio selection. - Serena Crisci; Valentina De Simone; Monica Pragliola; Gerardo Toraldo (J. Comput. Appl. Math., 2025)

Sparsity-promoting hierarchical Bayesian model for EIT with a blocky target. - Daniela Calvetti; Monica Pragliola; Erkki Somersalo (J. Comput. Phys., 2025)

A general framework for whiteness-based parameters selection in variational models. - Francesca Bevilacqua; Alessandro Lanza; Monica Pragliola; Fiorella Sgallari (Comput. Optim. Appl., 2025)