| Global sensitivity analysis with dependence measures S Da Veiga Journal of Statistical Computation and Simulation 85 (7), 1283-1305, 2015 | 172 | 2015 |
| Local polynomial estimation for sensitivity analysis on models with correlated inputs S Da Veiga, F Wahl, F Gamboa Technometrics 51 (4), 452-463, 2009 | 158 | 2009 |
| Global sensitivity analysis of stochastic computer models with joint metamodels A Marrel, B Iooss, S Da Veiga, M Ribatet Statistics and Computing 22, 833-847, 2012 | 148 | 2012 |
| Gaussian process modeling with inequality constraints S Da Veiga, A Marrel Annales de la Faculté des sciences de Toulouse: Mathématiques 21 (3), 529-555, 2012 | 93 | 2012 |
| Sensitivity: global sensitivity analysis of model outputs B Iooss, A Janon, G Pujol, B Broto, K Boumhaout, S Da Veiga, T Delage, ... R package version 1 (0), 2020 | 87 | 2020 |
| Interpretable random forests via rule extraction C Bénard, G Biau, S Da Veiga, E Scornet International Conference on Artificial Intelligence and Statistics, 937-945, 2021 | 56 | 2021 |
| Sensitivity: Global sensitivity analysis of model outputs G Pujol, B Iooss, A Janon, K Boumhaout, S Da Veiga, J Fruth, L Gilquin, ... R package version 1 (0), 2017 | 47 | 2017 |
| Appropriate formulation of the objective function for the history matching of seismic attributes E Tillier, S Da Veiga, R Derfoul Computers & Geosciences 51, 64-73, 2013 | 45 | 2013 |
| Global sensitivity analysis for optimization with variable selection A Spagnol, RL Riche, SD Veiga SIAM/ASA Journal on uncertainty quantification 7 (2), 417-443, 2019 | 37 | 2019 |
| Efficient estimation of sensitivity indices S Da Veiga, F Gamboa Journal of Nonparametric Statistics 25 (3), 573-595, 2013 | 37 | 2013 |
| Basics and trends in sensitivity analysis: theory and practice in R S Da Veiga, F Gamboa, B Iooss, C Prieur Society for Industrial and Applied Mathematics, 2021 | 34 | 2021 |
| Cosimulation as a perturbation method for calibrating porosity and permeability fields to dynamic data M Le Ravalec-Dupin, S Da Veiga Computers & geosciences 37 (9), 1400-1412, 2011 | 31 | 2011 |
| Sirus: Stable and interpretable rule set for classification C Bénard, G Biau, S Da Veiga, E Scornet | 26 | 2021 |
| Sensitivity: global sensitivity analysis of model outputs, R package version 1.26. 0 B Iooss, S Da Veiga, A Janon, G Pujol | 26 | 2020 |
| Advanced integrated workflows for incorporating both production and 4D seismic-related data into reservoir models M Le Ravalec, E Tillier, S Da Veiga, G Enchéry, V Gervais Oil & Gas Science and Technology–Revue d’IFP Energies nouvelles 67 (2), 207-220, 2012 | 26 | 2012 |
| Kernel-based ANOVA decomposition and Shapley effects--Application to global sensitivity analysis S Da Veiga arXiv preprint arXiv:2101.05487, 2021 | 22 | 2021 |
| Method of developing a petroleum reservoir from history matching of production data and seismic data R Derfoul, E Tillier, S Da Veiga US Patent 8,862,450, 2014 | 21 | 2014 |
| SHAFF: Fast and consistent SHApley eFfect estimates via random Forests C Bénard, G Biau, S Da Veiga, E Scornet International Conference on Artificial Intelligence and Statistics, 5563-5582, 2022 | 19 | 2022 |
| MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA C Bénard, S Da Veiga, E Scornet arXiv preprint arXiv:2102.13347, 2021 | 19 | 2021 |
| SIRUS: making random forests interpretable C Bénard, G Biau, S Da Veiga, E Scornet | 19 | 2019 |
