Mickael Binois
Mickael Binois
Inria Sophia Antipolis - Méditerranée
Verified email at - Homepage
Cited by
Cited by
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
Replication or exploration? Sequential design for stochastic simulation experiments
M Binois, J Huang, RB Gramacy, M Ludkovski
Technometrics 61 (1), 7-23, 2019
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
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
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
A Bayesian optimization approach to find Nash equilibria
V Picheny, M Binois, A Habbal
Journal of Global Optimization 73 (1), 171-192, 2019
hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication
M Binois, RB Gramacy
R package version 1 (1), 2017
GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis
M Binois, V Picheny
Journal of Statistical Software 89 (8), 2019
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
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
Sequential learning of active subspaces
N Wycoff, M Binois, SM Wild
Journal of Computational and Graphical Statistics, 1-33, 2021
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
The Kalai-Smorodinsky solution for many-objective Bayesian optimization.
M Binois, V Picheny, P Taillandier, A Habbal
J. Mach. Learn. Res. 21 (150), 1-42, 2020
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation
X Lyu, M Binois, M Ludkovski
arXiv preprint arXiv:1807.06712, 2018
GPareto: Gaussian processes for pareto front estimation and optimization
M Binois, V Picheny
R package version 1 (2), 2016
hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R
M Binois, RB Gramacy
Multiobjective statistical learning optimization of RGB metalens
MMR Elsawy, A Gourdin, M Binois, R Duvigneau, D Felbacq, S Khadir, ...
ACS Photonics 8 (8), 2498-2508, 2021
DiceOptim: Kriging-based optimization for computer experiments
V Picheny, D Ginsbourger, O Roustant, M Binois, S Marmin, T Wagner
R package version 0.8-1, 2016
A population data-driven workflow for COVID-19 modeling and learning
J Ozik, JM Wozniak, N Collier, CM Macal, M Binois
The International Journal of High Performance Computing Applications 35 (5 …, 2021
hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication, 2019
M Binois, RB Gramacy
URL https://CRAN. R-project. org/package= hetGP. R package version 1 (2), 0
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