| 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 | 209 | 2018 |
| Replication or exploration? Sequential design for stochastic simulation experiments M Binois, J Huang, RB Gramacy, M Ludkovski Technometrics 61 (1), 7-23, 2019 | 135 | 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 | 88 | 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 | 83 | 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 | 66 | 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 | 41 | 2015 |
| A Bayesian optimization approach to find Nash equilibria V Picheny, M Binois, A Habbal Journal of Global Optimization 73, 171-192, 2019 | 37 | 2019 |
| hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R M Binois, RB Gramacy | 36* | |
| GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis M Binois, V Picheny Journal of Statistical Software 89 (8), 2019 | 35 | 2019 |
| 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 | 34 | 2021 |
| hetGP: Heteroskedastic Gaussian Process Modeling and Design under Replication M Binois, RB Gramacy R package version 1 (1), 2017 | 33 | 2017 |
| 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 | 30 | 2021 |
| Sequential learning of active subspaces N Wycoff, M Binois, SM Wild Journal of Computational and Graphical Statistics, 1-33, 2021 | 30 | 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 | 22 | 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 |
| Optimization of metasurfaces under geometrical uncertainty using statistical learning MMR Elsawy, M Binois, R Duvigneau, S Lanteri, P Genevet Optics Express 29 (19), 29887-29898, 2021 | 14 | 2021 |
| 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 | 14 | 2020 |
Robert B. GramacyVirginia Tech在 vt.edu 的电子邮件经过验证
