output
20092025
most citedProbabilistic Time Series Forecasting with Structured Shape and Temporal Diversity

20 citations

13 papers

cs.AI2025

Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation

Frédérick Fabre Ferber, Dominique Gay, Jean-Christophe Soulié +2

Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techni…

stat.ME2023

A comparison between Bayesian and ordinary kriging based on validation criteria: application to radiological characterisation

Martin Wieskotten, Marielle Crozet, Bertrand Iooss +2

In decommissioning projects of nuclear facilities, the radiological characterisation step aims to estimate the quantity and spatial distribution of different radionuclides. To carr…

math.ST2022★ 1 cited

Proportional marginal effects for global sensitivity analysis

Margot Herin, Marouane Il Idrissi, Vincent Chabridon +1

Performing (variance-based) global sensitivity analysis (GSA) with dependent inputs has recently benefited from cooperative game theory concepts.By using this theory, despite the p…

math.ST2022★ 4 cited

Model predictivity assessment: incremental test-set selection and accuracy evaluation

Elias Fekhari, Bertrand Iooss, Joseph Muré +2

Unbiased assessment of the predictivity of models learnt by supervised machine-learning methods requires knowledge of the learned function over a reserved test set (not used by the…

math.AP2021★ 3 cited

Existence of weak solutions to the two-dimensional incompressible Euler equations in the presence of sources and sinks

Marco Bravin, Franck Sueur

A classical model for sources and sinks in a two-dimensional perfect incompressible fluid occupying a bounded domain dates back to Yudovich in 1966. In this model, on the one hand,…

math.ST2021★ 1 cited

Developments and applications of Shapley effects to reliability-oriented sensitivity analysis with correlated inputs

Marouane Il Idrissi, Vincent Chabridon, Bertrand Iooss

Reliability-oriented sensitivity analysis methods have been developed for understanding the influence of model inputs relative to events which characterize the failure of a system…