| Year |
Citation |
Score |
| 2020 |
Torossian L, Picheny V, Faivre R, Garivier A. A review on quantile regression for stochastic computer experiments Reliability Engineering & System Safety. 201: 106858. DOI: 10.1016/J.Ress.2020.106858 |
0.392 |
|
| 2020 |
Bachoc F, Helbert C, Picheny V. Gaussian process optimization with failures: classification and convergence proof Journal of Global Optimization. 1-24. DOI: 10.1007/S10898-020-00920-0 |
0.47 |
|
| 2020 |
Gaudrie D, Riche RL, Picheny V, Enaux B, Herbert V. Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions Annals of Mathematics and Artificial Intelligence. 88: 187-212. DOI: 10.1007/S10472-019-09644-8 |
0.406 |
|
| 2020 |
Gaudrie D, Riche RL, Picheny V, Enaux B, Herbert V. Modeling and Optimization with Gaussian Processes in Reduced Eigenbases Structural and Multidisciplinary Optimization. 61: 2343-2361. DOI: 10.1007/S00158-019-02458-6 |
0.42 |
|
| 2019 |
Binois M, Picheny V. GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis Journal of Statistical Software. 89: 1-30. DOI: 10.18637/Jss.V089.I08 |
0.451 |
|
| 2019 |
Picheny V, Servien R, Villa-Vialaneix N. Interpretable sparse SIR for functional data Statistics and Computing. 29: 255-267. DOI: 10.1007/S11222-018-9806-6 |
0.388 |
|
| 2019 |
Picheny V, Binois M, Habbal A. A Bayesian optimization approach to find Nash equilibria Journal of Global Optimization. 73: 171-192. DOI: 10.1007/S10898-018-0688-0 |
0.437 |
|
| 2018 |
Labopin-Richard T, Picheny V. Sequential design of experiments for estimating percentiles of black-box functions Statistica Sinica. 28. DOI: 10.5705/Ss.202016.0160 |
0.402 |
|
| 2018 |
Champion M, Picheny V, Vignes M. Inferring large graphs using $$\ell _1$$-penalized likelihood Statistics and Computing. 28: 905-921. DOI: 10.1007/S11222-017-9769-Z |
0.369 |
|
| 2017 |
Picheny V, Casadebaig P, Trépos R, Faivre R, Da Silva D, Vincourt P, Costes E. Using numerical plant models and phenotypic correlation space to design achievable ideotypes. Plant, Cell & Environment. PMID 28626887 DOI: 10.1111/Pce.13001 |
0.378 |
|
| 2017 |
Picheny V, Trépos R, Casadebaig P. Optimization of black-box models with uncertain climatic inputs-Application to sunflower ideotype design. Plos One. 12: e0176815. PMID 28542198 DOI: 10.1371/Journal.Pone.0176815 |
0.473 |
|
| 2017 |
Jalali H, Nieuwenhuyse IV, Picheny V. Comparison of Kriging-based algorithms for simulation optimization with heterogeneous noise European Journal of Operational Research. 261: 279-301. DOI: 10.1016/J.Ejor.2017.01.035 |
0.387 |
|
| 2016 |
Picheny V, Ginsbourger D, Krityakierne T. Comment: Some Enhancements Over the Augmented Lagrangian Approach Technometrics. 58: 17-21. DOI: 10.1080/00401706.2015.1079246 |
0.399 |
|
| 2015 |
Casadebaig P, Poublan B, Trepos R, Picheny V, Debaeke P. Using Plant Phenotypic Plasticity to Improve Crop Performance and Stability Regarding Climatic Uncertainty: A Computational Study on Sunflower Procedia Environmental Sciences. 29: 142-143. DOI: 10.1016/J.Proenv.2015.07.229 |
0.355 |
|
| 2015 |
Picheny V. Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction Statistics and Computing. 25: 1265-1280. DOI: 10.1007/S11222-014-9477-X |
0.462 |
|
| 2014 |
Chevalier C, Bect J, Ginsbourger D, Vazquez E, Picheny V, Richet Y. Fast Parallel Kriging-Based Stepwise Uncertainty Reduction With Application to the Identification of an Excursion Set Technometrics. 56: 455-465. DOI: 10.1080/00401706.2013.860918 |
0.373 |
|
| 2014 |
Picheny V, Ginsbourger D. Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package Computational Statistics & Data Analysis. 71: 1035-1053. DOI: 10.1016/J.Csda.2013.03.018 |
0.426 |
|
| 2014 |
Chevalier C, Picheny V, Ginsbourger D. KrigInv: An efficient and user-friendly implementation of batch-sequential inversion strategies based on kriging Computational Statistics & Data Analysis. 71: 1021-1034. DOI: 10.1016/J.Csda.2013.03.008 |
0.454 |
|
| 2013 |
Richet Y, Caplin G, Crevel J, Ginsbourger D, Picheny V. Using the Efficient Global Optimization Algorithm to Assist Nuclear Criticality Safety Assessment Nuclear Science and Engineering. 175: 1-18. DOI: 10.13182/Nse11-116 |
0.347 |
|
| 2013 |
Picheny V, Ginsbourger D. A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations Siam/Asa Journal On Uncertainty Quantification. 1: 57-78. DOI: 10.1137/120882834 |
0.414 |
|
| 2013 |
Picheny V, Ginsbourger D, Richet Y, Caplin G. Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision Technometrics. 55: 2-13. DOI: 10.1080/00401706.2012.707580 |
0.461 |
|
| 2013 |
Picheny V, Wagner T, Ginsbourger D. A benchmark of kriging-based infill criteria for noisy optimization Structural and Multidisciplinary Optimization. 48: 607-626. DOI: 10.1007/S00158-013-0919-4 |
0.447 |
|
| 2012 |
Bect J, Ginsbourger D, Li L, Picheny V, Vazquez E. Sequential design of computer experiments for the estimation of a probability of failure Statistics and Computing. 22: 773-793. DOI: 10.1007/S11222-011-9241-4 |
0.405 |
|
| 2010 |
Viana FAC, Picheny V, Haftka RT. Using Cross Validation to Design Conservative Surrogates Aiaa Journal. 48: 2286-2298. DOI: 10.2514/1.J050327 |
0.526 |
|
| 2010 |
Picheny V, Ginsbourger D, Roustant O, Haftka RT, Kim N. Adaptive Designs of Experiments for Accurate Approximation of a Target Region Journal of Mechanical Design. 132. DOI: 10.1115/1.4001873 |
0.526 |
|
| 2009 |
Picheny V, Kim NH, Haftka RT. Application of bootstrap method in conservative estimation of reliability with limited samples Structural and Multidisciplinary Optimization. 41: 205-217. DOI: 10.1007/S00158-009-0419-8 |
0.518 |
|
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