| A benchmark of kriging-based infill criteria for noisy optimization V Picheny, T Wagner, D Ginsbourger Structural and multidisciplinary optimization 48, 607-626, 2013 | 464 | 2013 |
| Sequential design of computer experiments for the estimation of a probability of failure J Bect, D Ginsbourger, L Li, V Picheny, E Vazquez Statistics and Computing 22, 773-793, 2012 | 397 | 2012 |
| Adaptive designs of experiments for accurate approximation of a target region V Picheny, D Ginsbourger, O Roustant, RT Haftka, NH Kim | 376 | 2010 |
| Quantile-based optimization of noisy computer experiments with tunable precision V Picheny, D Ginsbourger, G Richet, Yann, Caplin Technometrics, 2013 | 286* | 2013 |
| Fast parallel kriging-based stepwise uncertainty reduction with application to the identification of an excursion set C Chevalier, J Bect, D Ginsbourger, E Vazquez, V Picheny, Y Richet Technometrics 56 (4), 455-465, 2014 | 187 | 2014 |
| Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction V Picheny Statistics and Computing 25 (6), 1265-1280, 2015 | 167 | 2015 |
| On information gain and regret bounds in gaussian process bandits S Vakili, K Khezeli, V Picheny International Conference on Artificial Intelligence and Statistics, 82-90, 2021 | 147 | 2021 |
| Comparison of kriging-based algorithms for simulation optimization with heterogeneous noise H Jalali, I Van Nieuwenhuyse, V Picheny European Journal of Operational Research 261 (1), 279-301, 2017 | 131 | 2017 |
| Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian V Picheny, RB Gramacy, S Wild, S Le Digabel Advances in neural information processing systems 29, 2016 | 131 | 2016 |
| Application of bootstrap method in conservative estimation of reliability with limited samples V Picheny, NH Kim, RT Haftka Structural and Multidisciplinary Optimization 41, 205-217, 2010 | 101 | 2010 |
| A stepwise uncertainty reduction approach to constrained global optimization V Picheny Artificial intelligence and statistics, 787-795, 2014 | 95 | 2014 |
| Improving accuracy and compensating for uncertainty in surrogate modeling V Picheny HAL 2009, 2009 | 69 | 2009 |
| Noisy kriging-based optimization methods: a unified implementation within the DiceOptim package V Picheny, D Ginsbourger Computational Statistics & Data Analysis 71, 1035-1053, 2014 | 66 | 2014 |
| Using cross validation to design conservative surrogates FA C. Viana, V Picheny, RT Haftka Aiaa Journal 48 (10), 2286-2298, 2010 | 56 | 2010 |
| Gaussian process optimization with simulation failures: classification and convergence proof F Bachoc, C Helbert, V Picheny Journal of Global Optimization 2020, 2019 | 49* | 2019 |
| Kriginv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging C Chevalier, V Picheny, D Ginsbourger Computational statistics & data analysis 71, 1021-1034, 2014 | 49* | 2014 |
| A nonstationary space-time Gaussian process model for partially converged simulations V Picheny, D Ginsbourger SIAM/ASA Journal on Uncertainty Quantification 1 (1), 57-78, 2013 | 49 | 2013 |
| Scalable Thompson sampling using sparse Gaussian process models S Vakili, H Moss, A Artemev, V Dutordoir, V Picheny Advances in neural information processing systems 34, 5631-5643, 2021 | 46 | 2021 |
| GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis M Binois, V Picheny Journal of Statistical Software 89 (8), 2019 | 46* | 2019 |
| TREGO: a trust-region framework for efficient global optimization Y Diouane, V Picheny, RL Riche, ASD Perrotolo Journal of Global Optimization 86 (1), 1-23, 2023 | 45 | 2023 |
David GinsbourgerUniversity of BernVerified email at stat.unibe.ch