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ORCIDMickaël Binois
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2020 – today
- 2024
[i5]Khadija Musayeva, Mickaël Binois:
Shared active subspace for multivariate vector-valued functions. CoRR abs/2401.02735 (2024)- 2023
- [c5]Nicholson T. Collier, Justin M. Wozniak, Abby Stevens, Yadu N. Babuji, Mickaël Binois, Arindam Fadikar, Alexandra Würth, Kyle Chard, Jonathan Ozik:
Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis. IPDPS Workshops 2023: 868-877 - [c4]Alexandra Würth, Mickaël Binois, Paola Goatin:
Validation of Calibration Strategies for Macroscopic Traffic Flow Models on Synthetic Data. MT-ITS 2023: 1-6 - [c3]Khadija Musayeva, Mickaël Binois:
Improved Multi-label Propagation for Small Data with Multi-objective Optimization. ECML/PKDD (4) 2023: 284-300 - [c2]Arindam Fadikar, Nicholson T. Collier, Abby Stevens, Jonathan Ozik, Mickaël Binois, Kok Ben Toh:
Trajectory-Oriented Optimization of Stochastic Epidemiological Models. WSC 2023: 1244-1255 - [p1]Mickaël Binois, Abderrahmane Habbal, Victor Picheny:
A Game Theoretic Perspective on Bayesian Many-Objective Optimization. Many-Criteria Optimization and Decision Analysis 2023: 299-316 - [i4]Nicholson T. Collier, Justin M. Wozniak, Abby Stevens, Yadu N. Babuji, Mickaël Binois, Arindam Fadikar, Alexandra Würth, Kyle Chard, Jonathan Ozik:
Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis. CoRR abs/2304.14244 (2023) - 2022
- [j15]Alexandra Würth, Mickaël Binois, Paola Goatin, Simone Göttlich
:
Data-driven uncertainty quantification in macroscopic traffic flow models. Adv. Comput. Math. 48(6): 75 (2022) - [j14]Stefano Pezzano, Régis Duvigneau, Mickaël Binois:
Geometrically consistent aerodynamic optimization using an isogeometric Discontinuous Galerkin method. Comput. Math. Appl. 128: 368-381 (2022) - [j13]Nathan Wycoff, Mickaël Binois, Robert B. Gramacy:
Sensitivity Prewarping for Local Surrogate Modeling. Technometrics 64(4): 535-547 (2022) - [j12]Mickaël Binois, Nathan Wycoff:
A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization. ACM Trans. Evol. Learn. Optim. 2(2): 8:1-8:26 (2022) - 2021
- [j11]Jonathan Ozik, Justin M. Wozniak, Nicholson T. Collier, Charles M. Macal, Mickaël Binois:
A population data-driven workflow for COVID-19 modeling and learning. Int. J. High Perform. Comput. Appl. 35(5) (2021) - [j10]Nathan Wycoff, Mickaël Binois, Stefan M. Wild:
Sequential Learning of Active Subspaces. J. Comput. Graph. Stat. 30(4): 1224-1237 (2021) - [j9]Mickaël Binois, Robert B. Gramacy:
hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R. J. Stat. Softw. 98(1) (2021) - [j8]Xiong Lyu, Mickaël Binois, Michael Ludkovski:
Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation. Stat. Comput. 31(4): 43 (2021) - [i3]Nathan Wycoff, Mickaël Binois, Robert B. Gramacy:
Sensitivity Prewarping for Local Surrogate Modeling. CoRR abs/2101.06296 (2021) - 2020
- [j7]Mickaël Binois, David Ginsbourger, Olivier Roustant:
On the choice of the low-dimensional domain for global optimization via random embeddings. J. Glob. Optim. 76(1): 69-90 (2020) - [j6]Mickaël Binois, Victor Picheny, Patrick Taillandier, Abderrahmane Habbal:
The Kalai-Smorodinsky solution for many-objective Bayesian optimization. J. Mach. Learn. Res. 21: 150:1-150:42 (2020)
2010 – 2019
- 2019
- [j5]Victor Picheny, Mickaël Binois, Abderrahmane Habbal:
A Bayesian optimization approach to find Nash equilibria. J. Glob. Optim. 73(1): 171-192 (2019) - [j4]Matthias Chung, Mickaël Binois, Robert B. Gramacy, Johnathan M. Bardsley, David J. Moquin, Amanda P. Smith, Amber M. Smith:
Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes. SIAM J. Sci. Comput. 41(4): A2212-A2238 (2019) - [j3]Mickaël Binois, Jiangeng Huang, Robert B. Gramacy, Mike Ludkovski:
Replication or Exploration? Sequential Design for Stochastic Simulation Experiments. Technometrics 61(1): 7-23 (2019) - [i2]Nathan Wycoff, Mickaël Binois, Stefan M. Wild:
Sequential Learning of Active Subspaces. CoRR abs/1907.11572 (2019) - 2018
- [i1]Xiong Lyu, Mickaël Binois, Michael Ludkovski:
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation. CoRR abs/1807.06712 (2018) - 2015
- [j2]Mickaël Binois, David Ginsbourger, Olivier Roustant:
Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations. Eur. J. Oper. Res. 243(2): 386-394 (2015) - [j1]Mickaël Binois, Didier Rullière, Olivier Roustant:
On the estimation of Pareto fronts from the point of view of copula theory. Inf. Sci. 324: 270-285 (2015) - [c1]Mickaël Binois, David Ginsbourger, Olivier Roustant:
A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings. LION 2015: 281-286
Coauthor Index
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