Mickaël Binois

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

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Shared active subspace for multivariate vector-valued functions.

Khadija Musayeva, Mickaël Binois

CoRR, 2024

2023

A Game Theoretic Perspective on Bayesian Many-Objective Optimization.

Mickaël Binois, Abderrahmane Habbal, Victor Picheny

Proceedings of the Many-Criteria Optimization and Decision Analysis: State-of-the-Art, 2023

Trajectory-Oriented Optimization of Stochastic Epidemiological Models.

Arindam Fadikar, Nicholson T. Collier, Abby Stevens, Jonathan Ozik, Mickaël Binois, Kok Ben Toh

Proceedings of the Winter Simulation Conference, 2023

Improved Multi-label Propagation for Small Data with Multi-objective Optimization.

Khadija Musayeva, Mickaël Binois

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.

Alexandra Würth, Mickaël Binois, Paola Goatin

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.

Nicholson T. Collier, Justin M. Wozniak, Abby Stevens, Yadu N. Babuji, Mickaël Binois, Arindam Fadikar, Alexandra Würth, Kyle Chard, Jonathan Ozik

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.

Mickaël Binois, Nathan Wycoff

ACM Trans. Evol. Learn. Optim., 2022

Sensitivity Prewarping for Local Surrogate Modeling.

Nathan Wycoff, Mickaël Binois, Robert B. Gramacy

Technometrics, 2022

Geometrically consistent aerodynamic optimization using an isogeometric Discontinuous Galerkin method.

Stefano Pezzano, Régis Duvigneau, Mickaël Binois

Comput. Math. Appl., 2022

Data-driven uncertainty quantification in macroscopic traffic flow models.

Alexandra Würth, Mickaël Binois, Paola Goatin, Simone Göttlich

Adv. Comput. Math., 2022

2021

Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation.

Xiong Lyu, Mickaël Binois, Michael Ludkovski

Stat. Comput., 2021

hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R.

Mickaël Binois, Robert B. Gramacy

J. Stat. Softw., 2021

Sequential Learning of Active Subspaces.

Nathan Wycoff, Mickaël Binois, Stefan M. Wild

J. Comput. Graph. Stat., 2021

A population data-driven workflow for COVID-19 modeling and learning.

Jonathan Ozik, Justin M. Wozniak, Nicholson T. Collier, Charles M. Macal, Mickaël Binois

Int. J. High Perform. Comput. Appl., 2021

2020

The Kalai-Smorodinsky solution for many-objective Bayesian optimization.

Mickaël Binois, Victor Picheny, Patrick Taillandier, Abderrahmane Habbal

J. Mach. Learn. Res., 2020

On the choice of the low-dimensional domain for global optimization via random embeddings.

Mickaël Binois, David Ginsbourger, Olivier Roustant

J. Glob. Optim., 2020

2019

Replication or Exploration? Sequential Design for Stochastic Simulation Experiments.

Mickaël Binois, Jiangeng Huang, Robert B. Gramacy, Mike Ludkovski

Technometrics, 2019

Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes.

Matthias Chung, Mickaël Binois, Robert B. Gramacy, Johnathan M. Bardsley, David J. Moquin, Amanda P. Smith, Amber M. Smith

SIAM J. Sci. Comput., 2019

A Bayesian optimization approach to find Nash equilibria.

Victor Picheny, Mickaël Binois, Abderrahmane Habbal

J. Glob. Optim., 2019

2015

On the estimation of Pareto fronts from the point of view of copula theory.

Mickaël Binois, Didier Rullière, Olivier Roustant

Inf. Sci., 2015

Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations.

Mickaël Binois, David Ginsbourger, Olivier Roustant

Eur. J. Oper. Res., 2015

A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings.

Mickaël Binois, David Ginsbourger, Olivier Roustant

Proceedings of the Learning and Intelligent Optimization - 9th International Conference, 2015