Mickael Binois
Mickael Binois
Inria Sophia Antipolis - Méditerranée
Adresse e-mail validée de inria.fr - Page d'accueil
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Practical heteroscedastic gaussian process modeling for large simulation experiments
M Binois, RB Gramacy, M Ludkovski
Journal of Computational and Graphical Statistics 27 (4), 808-821, 2018
962018
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
562015
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
562015
Replication or exploration? Sequential design for stochastic simulation experiments
M Binois, J Huang, RB Gramacy, M Ludkovski
Technometrics 61 (1), 7-23, 2019
542019
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
242015
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
202020
On the estimation of Pareto fronts from the point of view of copula theory
M Binois, D Rullière, O Roustant
Information Sciences 324, 270-285, 2015
172015
Uncertainty quantification on pareto fronts and high-dimensional strategies in bayesian optimization, with applications in multi-objective automotive design
M Binois
Ecole Nationale Supérieure des Mines de Saint-Etienne, 2015
152015
hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication
M Binois, RB Gramacy
R package version 1 (1), 2017
142017
Parameter and uncertainty estimation for dynamical systems using surrogate stochastic processes
M Chung, M Binois, RB Gramacy, JM Bardsley, DJ Moquin, AP Smith, ...
SIAM Journal on Scientific Computing 41 (4), A2212-A2238, 2019
132019
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation
X Lyu, M Binois, M Ludkovski
arXiv preprint arXiv:1807.06712, 2018
122018
GPareto: Gaussian processes for pareto front estimation and optimization
M Binois, V Picheny
R package version 1 (2), 2016
122016
A Bayesian optimization approach to find Nash equilibria
V Picheny, M Binois, A Habbal
Journal of Global Optimization 73 (1), 171-192, 2019
112019
A Bayesian optimization approach to find Nash equilibria
V Picheny, M Binois, A Habbal
Journal of Global Optimization 73 (1), 171-192, 2019
112019
A Bayesian optimization approach to find Nash equilibria
V Picheny, M Binois, A Habbal
arXiv preprint arXiv:1611.02440, 2016
102016
Sequential learning of active subspaces
N Wycoff, M Binois, SM Wild
Journal of Computational and Graphical Statistics, 1-33, 2021
72021
GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis
M Binois, V Picheny
Journal of Statistical Software 89 (8), 2019
72019
On‐site surrogates for large‐scale calibration
J Huang, RB Gramacy, M Binois, M Libraschi
Applied Stochastic Models in Business and Industry 36 (2), 283-304, 2020
52020
GPareto: Gaussian Processes for Pareto Front Estimation and Optimization, 2015
M Binois, V Picheny
URL http://CRAN. R-project. org/package= GPareto. R package version 1 (1), 0
5
The Kalai-Smorodinsky solution for many-objective Bayesian optimization
M Binois, V Picheny, P Taillandier, A Habbal
Journal of Machine Learning Research 21 (150), 1-42, 2020
42020
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