Victor Picheny
Victor Picheny
Director of Research, Secondmind
Verified email at - Homepage
Cited by
Cited by
A benchmark of kriging-based infill criteria for noisy optimization
V Picheny, T Wagner, D Ginsbourger
Structural and multidisciplinary optimization 48, 607-626, 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
Adaptive designs of experiments for accurate approximation of a target region
V Picheny, D Ginsbourger, O Roustant, RT Haftka, NH Kim
Quantile-based optimization of noisy computer experiments with tunable precision
V Picheny, D Ginsbourger, G Richet, Yann, Caplin
Technometrics, 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
Multiobjective optimization using Gaussian process emulators via stepwise uncertainty reduction
V Picheny
Statistics and Computing 25 (6), 1265-1280, 2015
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
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
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
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
A stepwise uncertainty reduction approach to constrained global optimization
V Picheny
Artificial intelligence and statistics, 787-795, 2014
Improving accuracy and compensating for uncertainty in surrogate modeling
V Picheny
Saint-Etienne, EMSE, 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
Using cross validation to design conservative surrogates
FA C. Viana, V Picheny, RT Haftka
Aiaa Journal 48 (10), 2286-2298, 2010
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
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
Using numerical plant models and phenotypic correlation space to design achievable ideotypes
V Picheny, P Casadebaig, R Trépos, R Faivre, D Da Silva, P Vincourt, ...
Plant, Cell & Environment 40 (9), 1926-1939, 2017
Conservative predictions using surrogate modeling
V Picheny, NH Kim, R Haftka, N Queipo
49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials …, 2008
A Bayesian optimization approach to find Nash equilibria
V Picheny, M Binois, A Habbal
Journal of Global Optimization 73, 171-192, 2019
GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis
M Binois, V Picheny
Journal of Statistical Software 89 (8), 2019
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