MCMC-driven importance samplers. - Fernando Llorente 001; Ernesto Curbelo; Luca Martino; Victor Elvira; David Delgado 001 (CoRR, 2021)

Automatic tempered posterior distributions for Bayesian inversion problems. - Luca Martino; Fernando Llorente 001; Ernesto Curbelo; Javier López-Santiago; Joaquín Míguez (CoRR, 2021)

Safe importance sampling based on partial posteriors and neural variational approximations. - Fernando Llorente 001; Ernesto Curbelo; Luca Martino; Pablo M. Olmos; David Delgado 001 (EUSIPCO, 2022)

Neural lasso: a unifying approach of lasso and neural networks. - David Delgado 001; Ernesto Curbelo; Danae Carreras-García (CoRR, 2023)

Adaptive posterior distributions for uncertainty analysis of covariance matrices in Bayesian inversion problems for multioutput signals. - Ernesto Curbelo; Luca Martino; Fernando Llorente 001; David Delgado-Gómez (J. Frankl. Inst., 2025)

pyLAIS: A Python package for Layered Adaptive Importance Sampling. - Ernesto Curbelo; Luca Martino; David Delgado-Gómez (SoftwareX, 2025)

Corrigendum to "pyLAIS: A Python package for Layered Adaptive Importance Sampling" [SoftwareX 29 (2025), 101976]. - Ernesto Curbelo; Luca Martino; David Delgado-Gómez (SoftwareX, 2025)

Enhancing Graphical Lasso: A Robust Scheme for Non-Stationary Mean Data. - Samuel Rey; Ernesto Curbelo; Luca Martino; Fernando Llorente 001; Antonio G. Marques (CoRR, 2025)

Neural lasso: a unifying approach of lasso and neural networks. - Ernesto Curbelo; David Delgado-Gómez; Danae Carreras (Int. J. Data Sci. Anal., 2025)

An automatic counting algorithm for the quantification and uncertainty analysis of the number of microglial cells trainable in small and heterogeneous datasets. - Luca Martino; M. M. Garcia; P. S. Paradas; Ernesto Curbelo (Expert Syst. Appl., 2026)