Mickael Binois

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Robert B. GramacyVirginia TechVerified email at vt.edu
Victor PichenyDirector of Research, SecondmindVerified email at secondmind.ai
Regis DuvigneauINRIA Sophia-AntipolisVerified email at inria.fr
Mike LudkovskiProfessor, UC Santa BarbaraVerified email at pstat.ucsb.edu
Olivier RoustantProfessor, INSA Toulouse, FranceVerified email at insa-toulouse.fr
David GinsbourgerUniversity of BernVerified email at stat.unibe.ch
Patrice GenevetProfessor of Physics, Colorado School of Mines, Colorado, USAVerified email at mines.edu
Nathan WycoffDepartment of Mathematics and Statistics, University of Massachusetts AmherstVerified email at umass.edu
Mahmoud ElsawyUniversité Côte d’Azur, Inria, CNRS, LJAD, 06902 Sophia Antipolis Cedex, FranceVerified email at inria.fr
Abderrahmane HabbalUniversity Nice Sophia Antipolis and INRIAVerified email at unice.fr
Jiangeng HuangAbbVieVerified email at abbvie.com
Jonathan OzikArgonne National LaboratoryVerified email at anl.gov
Justin M WozniakComputer Scientist, Argonne National LaboratoryVerified email at anl.gov
Stephane LanteriDr Stéphane LanteriVerified email at inria.fr
goatin paolaINRIAVerified email at inria.fr
Stefan M. WildBerkeley Lab, Applied Mathematics & Computational Research DivisionVerified email at lbl.gov
Simone GöttlichProfessor of Mathematics, University of MannheimVerified email at uni-mannheim.de
Arindam FadikarAssistant Computational StatisticianVerified email at anl.gov
Alexandra WürthPhD Student at Centre Inria d'Université Côte d'AzurVerified email at inria.fr
Abby StevensStatistician, Argonne National LaboratoryVerified email at uchicago.edu

Mickael Binois

Inria Sophia Antipolis - Méditerranée

Verified email at inria.fr - Homepage

Bayesian Optimization Gaussian processes computer experiments


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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	222	2018
Replication or exploration? Sequential design for stochastic simulation experiments M Binois, J Huang, RB Gramacy, M Ludkovski Technometrics 61 (1), 7-23, 2019	141	2019
A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization M Binois, N Wycoff ACM Transactions on Evolutionary Learning and Optimization 2 (2), 1-26, 2022	103	2022
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	84	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	68	2020
A warped kernel improving robustness in Bayesian optimization via random embeddings M Binois, D Ginsbourger, O Roustant Learning and Intelligent Optimization: 9th International Conference, LION 9 …, 2015	42	2015
hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R M Binois, RB Gramacy	42*
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	39	2021
A Bayesian optimization approach to find Nash equilibria V Picheny, M Binois, A Habbal Journal of Global Optimization 73, 171-192, 2019	37	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	37	2019
hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication M Binois, RB Gramacy R package version 1 (1), 2017	33	2017
Sequential learning of active subspaces N Wycoff, M Binois, SM Wild Journal of Computational and Graphical Statistics, 1-33, 2021	32	2021
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	31	2021
Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation X Lyu, M Binois, M Ludkovski Statistics and Computing 31 (4), 1-21, 2021	24	2021
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	23	2015
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	21	2020
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	21	2015
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	19	2019
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	16	2020
DiceOptim: Kriging-based optimization for computer experiments V Picheny, D Ginsbourger, O Roustant, M Binois, S Marmin, T Wagner R package version 2, 2016	15	2016

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