Calibration of forcefields for molecular simulation: Sequential design of computer experiments for building cost-efficient kriging metamodels. - Fabien Cailliez; Arnaud Bourasseau; Pascal Pernot (J. Comput. Chem., 2014)

On the Use of Benchmarks for Multiple Properties. - Bartolomeo Civalleri; Roberto Dovesi; Pascal Pernot; Davide Presti; Andreas Savin (Comput., 2016)

Impact of non-normal error distributions on the benchmarking and ranking of quantum machine learning models. - Pascal Pernot; Bing Huang; Andreas Savin (Mach. Learn. Sci. Technol., 2020)

Corrigendum: Impact of non-normal error distributions on the benchmarking and ranking of quantum machine learning models (2020 Mach. Learn.: Sci. Technol. 1 035011). - Pascal Pernot; Bing Huang; Andreas Savin (Mach. Learn. Sci. Technol., 2021)

Should We Gain Confidence from the Similarity of Results between Methods? - Pascal Pernot; Andreas Savin (Comput., 2022)

Properties of the ENCE and other MAD-based calibration metrics. - Pascal Pernot (CoRR, 2023)

Consistency and adaptivity are complementary targets for the validation of variance-based uncertainty quantification metrics in machine learning regression tasks. - Pascal Pernot (CoRR, 2023)

Can bin-wise scaling improve consistency and adaptivity of prediction uncertainty for machine learning regression ? - Pascal Pernot (CoRR, 2023)

How to validate average calibration for machine learning regression tasks ? - Pascal Pernot (CoRR, 2024)

Validation of ML-UQ calibration statistics using simulated reference values: a sensitivity analysis. - Pascal Pernot (CoRR, 2024)

On the good reliability of an interval-based metric to validate prediction uncertainty for machine learning regression tasks. - Pascal Pernot (CoRR, 2024)