S. Da Veiga. Distributional encoding for Gaussian process regression. Paper 

L. Allain, S. Da Veiga, and B. Staber. Scalable and adaptive prediction bands with kernel sum-of-squares. Paper 

S. Da Veiga, F. Gamboa, A. Lagnoux, T. Klein, and C. Prieur. Efficient estimation of Sobol’ indices of any order from a single input/output sample. Paper

G. Sarazin, A. Marrel, S. Da Veiga and V. Chabridon. New insights into the feature maps of Sobolev kernels: application in global sensitivity analysis. Paper Code 

B. Staber and S. Da Veiga. Benchmarking bayesian neural networks and evaluation metrics for regression tasks. Paper Code 

W. Piat, J. Fadili, F. Jurie, and S. Da Veiga. Regularized robust optimization with application to robust learning. Paper

S. Da Veiga. Kernel-based anova decomposition and shapley effects - application to global sensitivity analysis. Paper Code 

R. Carpintero Perez, S. Da Veiga, J. Garnier, B. Staber. Learning signals defined on graphs with optimal transport and Gaussian process regression. To appear in International Conference on Artificial Intelligence and Statistics (AISTATS), 2025. Paper

R. Carpintero Perez, S. Da Veiga, J. Garnier, B. Staber.  Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels. In International Conference on Artificial Intelligence and Statistics (AISTATS), pages 1297-1305. PMLR, 2024. Paper Datasets 

C. Bénard, B. Staber, and S. Da Veiga. Kernel stein discrepancy thinning: a theoretical perspective of pathologies and a practical fix with regularization. Advances in Neural Information Processing Systems, 2023. Paper Code 

R. E. Amri, R. Le Riche, C. Helbert, C. Blanchet-Scalliet, and S. Da Veiga. A sampling criterion for constrained bayesian optimization with uncertainties. The SMAI Journal of computational mathematics 9: 285-309, 2023. Paper

C. Laboulfie, M. Balesdent, L. Brevault, F.X. Irisarri, J.F. Le Maire, S. Da Veiga, and R. Le Riche. Sequential calibration of material constitutive model using mixed-effects calibration. Mechanics & Industry, 24, 2023. Paper

C. Bénard, S. Da Veiga, and E. Scornet. Mean decrease accuracy for random forests: inconsistency, and a practical solution via the sobol-mda. Biometrika, 109(4):881–900, 2022. Paper Code 

W. Piat, J. Fadili, F. Jurie, and S. Da Veiga. Towards an evaluation of lipschitz constant estimation algorithms by building models with a known lipschitz constant. In Workshop on Trustworthy Artificial Intelligence as a part of the ECML/PKDD 22 program, 2022. Paper

C. Bénard, S. Da Veiga, and E. Scornet. Interpretability via Random Forests. Lepore, Antonio, Biagio Palumbo, and Jean-Michel Poggi, eds. Interpretability for Industry 4.0: Statistical and Machine Learning Approaches. Springer Nature, 2022. Paper

C. Bénard, G. Biau, S. Da Veiga, and E. Scornet. Shaff: Fast and consistent shapley effect estimates via random forests. In International Conference on Artificial Intelligence and Statistics (AISTATS), pages 5563–5582. PMLR, 2022. Paper Code 

Guillaume Perrin and S. Da Veiga. Constrained gaussian process regression: an adaptive approach for the estimation of hyperparameters and the verification of constraints with high probability. Journal of Machine Learning for Modeling and Computing, 2(2), 2021. Paper

T.T. Tran, D. Sinoquet, S. Da Veiga, and M. Mongeau. Derivative-free mixed binary necklace optimization for cyclic-symmetry optimal design problems. Optimization and Engineering, pages 1–42, 2021. Paper

C. Bénard, G. Biau, S. Da Veiga, and E. Scornet. Interpretable random forests via rule extraction. In International Conference on Artificial Intelligence and Statistics (AISTATS), pages 937–945. PMLR, 2021. Paper Code 

C. Bénard, G. Biau, S. Da Veiga, and E. Scornet. Sirus: Stable and interpretable rule set for classification. Electronic Journal of Statistics, 15(1):427–505, 2021. Paper Code 

S. Da Veiga and A. Marrel. Gaussian process regression with linear inequality constraints. Reliability Engineering & System Safety, 195:106732, 2020. Paper

A. Spagnol, R. Le Riche, and S. Da Veiga. Bayesian optimization in effective dimensions via kernel-based sensitivity indices. In 30th European conference on operational research (EURO2019), 2019. Paper

R. Deswarte, V. Gervais, G. Stoltz, and S. Da Veiga. Sequential model aggregation for production forecasting. Computational Geosciences, 23(5):1107–1124, 2019. Paper

A. Spagnol, R. Le Riche, and S. Da Veiga. Global sensitivity analysis for optimization with variable selection. SIAM/ASA Journal on Uncertainty Quantification, 7(2):417–443, 2019. Paper

S. Da Veiga, J.M. Loubes, and M. Solís. Efficient estimation of conditional covariance matrices for dimension reduction. Communications in Statistics-Theory and Methods, 46(9):4403–4424, 2017. Paper

S. Da Veiga. Global sensitivity analysis with dependence measures. Journal of Statistical Computation and Simulation, 85(7):1283–1305, 2015. Paper Code 

D. Busby, S. Da Veiga, and S. Touzani. A workflow for decision making under uncertainty. Computational Geosciences, 18(3-4):519–533, 2014. Paper

F. Douarche, S. Da Veiga, M. Feraille, G. Enchéry, S. Touzani, and R. Barsalou. Sensitivity analysis and optimization of surfactant-polymer flooding under uncertainties. OGST, 69(4):603–617, 2014. Paper

E. Tillier, S. Da Veiga, and R. Derfoul. Appropriate formulation of the objective function for the history matching of seismic attributes. Computers and Geosciences, 51:64–73, 2013. Paper

S. Da Veiga and F. Gamboa. Efficient estimation of nonlinear conditional functionals of a density. Journal of Nonparametric Statistics, 25(3):573–595, 2013. Paper

R. Derfoul, S. Da Veiga, C. Gout, C. Le Guyader, and E. Tillier. Image processing tools for better incorporation of 4D seismic data in reservoir models. Journal of Computational and Applied Mathematics, 240:111–122, 2013. Paper

M. Le Ravalec, S. Da Veiga, R. Derfoul, G. Enchéry, V. Gervais, and F. Roggero. Integrating data of different types and different supports into reservoir models. OGST, 67(5):823–839, 2012. Paper

S. Da Veiga and M. Le Ravalec. Maximum-likelihood classification for facies inference from petrophysical realizations: Application to reservoir model reconstruction for history-matching. Computational Geosciences, 16(3):709–722, 2012. Paper

B. Iooss, A. Marrel, S. Da Veiga, and M. Ribatet. Global sensitivity analysis of stochastic computer models with joint metamodels. Statistics and Computing, 22(3):833–847, 2012. Paper

M. Le Ravalec, E. Tillier, S. Da Veiga, G. Enchéry, and V. Gervais. Advanced integrated workflows for incorporating both production and 4D seismic-related data into reservoir models. OGST, 67(2):207–220, 2012. Paper

E. Tillier, M. Le Ravalec, and S. Da Veiga. Simultaneous inversion of production data and seismic attributes: Application to a SAGD produced field. OGST, 67(2):289–301, 2012. Paper

S. Da Veiga and V. Gervais. Local adaptive parameterization for the history matching of 3D seismic data. Computational Geosciences, 16:483–498, 2012. Paper

S. Da Veiga and A. Marrel. Gaussian process modeling with inequality constraints. Annales de la faculté des sciences de Toulouse Sér. 6, 21(3):529–555, 2012. Paper

M. Le Ravalec, G. Enchéry, A. Baroni, and S. Da Veiga. Pre-selection of reservoir models from a geostatistics-based petrophysical seismic inversion. SPE Res Eval & Eng, 14(5):612–620, 2011. Paper

M. Le Ravalec-Dupin and S. Da Veiga. Cosimulation as a perturbation method for calibrating porosity and permeability fields to dynamic data. Computers and Geosciences, 37(9):1400–1412, 2011. Paper

S. Da Veiga, F. Wahl, and F. Gamboa. Local polynomial estimation for sensitivity analysis on models with correlated inputs. Technometrics, 51(4):452–463, 2009. Paper