| DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by kriging-based metamodeling and optimization O Roustant, D Ginsbourger, Y Deville Journal of statistical software 51, 1-55, 2012 | 785 | 2012 |
| Calculations of sobol indices for the gaussian process metamodel A Marrel, B Iooss, B Laurent, O Roustant Reliability Engineering & System Safety 94 (3), 742-751, 2009 | 409 | 2009 |
| Adaptive designs of experiments for accurate approximation of a target region V Picheny, D Ginsbourger, O Roustant, RT Haftka, NH Kim Journal of Mechanical Design 132 (7), 071008, 2010 | 370 | 2010 |
| Additive covariance kernels for high-dimensional Gaussian process modeling N Durrande, D Ginsbourger, O Roustant Annales de la Faculté de Sciences de Toulouse 21 (3), p. 481-499, 2012 | 154* | 2012 |
| ANOVA kernels and RKHS of zero mean functions for model-based sensitivity analysis N Durrande, D Ginsbourger, O Roustant, L Carraro Journal of Multivariate Analysis 115, 57-67, 2013 | 98 | 2013 |
| Finite-dimensional Gaussian approximation with linear inequality constraints AF López-Lopera, F Bachoc, N Durrande, O Roustant SIAM/ASA Journal on Uncertainty Quantification 6 (3), 1224-1255, 2018 | 92 | 2018 |
| Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations M Binois, D Ginsbourger, O Roustant European Journal of Operational Research 243 (2), 386-394, 2015 | 85 | 2015 |
| Group kernels for Gaussian process metamodels with categorical inputs O Roustant, E Padonou, Y Deville, A Clément, G Perrin, J Giorla, H Wynn SIAM/ASA Journal on Uncertainty Quantification 8 (2), 775-806, 2020 | 76 | 2020 |
| On the choice of the low-dimensional domain for global optimization via random embeddings M Binois, D Ginsbourger, O Roustant Journal of global optimization 76 (1), 69-90, 2020 | 71 | 2020 |
| Universal Prediction Distribution for Surrogate Models MB Salem, O Roustant, F Gamboa, L Tomaso SIAM/ASA Journal on Uncertainty Quantification 5 (1), 1086-1109, 2017 | 65 | 2017 |
| Data-driven Kriging models based on FANOVA-decomposition T Muehlenstaedt, O Roustant, L Carraro, S Kuhnt Statistics and Computing 22 (3), 723-738, 2012 | 64 | 2012 |
| Global sensitivity analysis for optimization with variable selection A Spagnol, R Le Riche, S Da Veiga, O Roustant PGMO Days 2017, 2017 | 56 | 2017 |
| Poincaré inequalities on intervals–application to sensitivity analysis O Roustant, F Barthe, B Iooss | 55 | 2016 |
| A note on the choice and the estimation of kriging models for the analysis of deterministic computer experiments D Ginsbourger, D Dupuy, A Badea, L Carraro, O Roustant Applied Stochastic Models in Business and Industry 25 (2), 115-131, 2009 | 53 | 2009 |
| DiceKriging: Kriging methods for computer experiments O Roustant, D Ginsbourger, Y Deville R package version 1, 2010 | 44* | 2010 |
| A warped kernel improving robustness in Bayesian optimization via random embeddings M Binois, D Ginsbourger, O Roustant International Conference on Learning and Intelligent Optimization, 281-286, 2015 | 43 | 2015 |
| Model risk in the pricing of weather derivatives O Roustant, JP Laurent, X Bay, L Carraro Banque & Marchés, 2003 | 40 | 2003 |
| Polar Gaussian Processes and Experimental Designs in Circular Domains E Padonou, O Roustant SIAM/ASA Journal on Uncertainty Quantification 4 (1), 1014-1033, 2016 | 39* | 2016 |
| On degeneracy and invariances of random fields paths with applications in Gaussian process modelling D Ginsbourger, O Roustant, N Durrande Journal of Statistical Planning and Inference, 2015 | 39* | 2015 |
| Crossed-derivative based sensitivity measures for interaction screening O Roustant, J Fruth, B Iooss, S Kuhnt Mathematics and Computers in Simulation 105, 105-118, 2014 | 38 | 2014 |
David GinsbourgerUniversity of BernVerified email at stat.unibe.ch
