Mickaël Binois

Orcid: 0000-0002-7225-1680

According to our database1, Mickaël Binois authored at least 22 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Shared active subspace for multivariate vector-valued functions.
CoRR, 2024

2023
A Game Theoretic Perspective on Bayesian Many-Objective Optimization.
Proceedings of the Many-Criteria Optimization and Decision Analysis: State-of-the-Art, 2023

Trajectory-Oriented Optimization of Stochastic Epidemiological Models.
Proceedings of the Winter Simulation Conference, 2023

Improved Multi-label Propagation for Small Data with Multi-objective Optimization.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Validation of Calibration Strategies for Macroscopic Traffic Flow Models on Synthetic Data.
Proceedings of the 8th International Conference on Models and Technologies for Intelligent Transportation Systems, 2023

Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis.
Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2023

2022
A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization.
ACM Trans. Evol. Learn. Optim., 2022

Sensitivity Prewarping for Local Surrogate Modeling.
Technometrics, 2022

Geometrically consistent aerodynamic optimization using an isogeometric Discontinuous Galerkin method.
Comput. Math. Appl., 2022

Data-driven uncertainty quantification in macroscopic traffic flow models.
Adv. Comput. Math., 2022

2021
Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation.
Stat. Comput., 2021

hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R.
J. Stat. Softw., 2021

Sequential Learning of Active Subspaces.
J. Comput. Graph. Stat., 2021

A population data-driven workflow for COVID-19 modeling and learning.
Int. J. High Perform. Comput. Appl., 2021

2020
The Kalai-Smorodinsky solution for many-objective Bayesian optimization.
J. Mach. Learn. Res., 2020

On the choice of the low-dimensional domain for global optimization via random embeddings.
J. Glob. Optim., 2020

2019
Replication or Exploration? Sequential Design for Stochastic Simulation Experiments.
Technometrics, 2019

Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes.
SIAM J. Sci. Comput., 2019

A Bayesian optimization approach to find Nash equilibria.
J. Glob. Optim., 2019

2015
On the estimation of Pareto fronts from the point of view of copula theory.
Inf. Sci., 2015

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations.
Eur. J. Oper. Res., 2015

A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings.
Proceedings of the Learning and Intelligent Optimization - 9th International Conference, 2015


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