Mickaël Binois

31 papers Misc 1Journal 24Unranked 5
YearRankTypeTitle / Venue / Authors
2026 J jnl
Optimal Control of Microswimmers for Trajectory Tracking Using Bayesian Optimization.
CoRR
Lucas Palazzolo, Mickaël Binois, Laëtitia Giraldi
2025 J jnl
A Portfolio Approach to Massively Parallel Bayesian Optimization.
J. Artif. Intell. Res.
Mickaël Binois, Nicholson T. Collier, Jonathan Ozik
2025 J jnl
Modular Jump Gaussian Processes.
CoRR
Anna R. Flowers, Christopher T. Franck, Mickaël Binois, Chiwoo Park, Robert B. Gramacy
2025 J jnl
hetGPy: Heteroskedastic Gaussian Process Modeling in Python.
J. Open Source Softw.
David O'Gara, Mickaël Binois, Roman Garnett, Ross A. Hammond
2024 — conf
Decoupled Design of Experiments for Expensive Multi-objective Problems.
LION
Mickaël Binois, Jürgen Branke, Jonathan E. Fieldsend, Robin C. Purshouse
2024 J jnl
Shared active subspace for multivariate vector-valued functions.
CoRR
Khadija Musayeva, Mickaël Binois
2023 — ch.
A Game Theoretic Perspective on Bayesian Many-Objective Optimization.
Many-Criteria Optimization and Decision Analysis
Mickaël Binois, Abderrahmane Habbal, Victor Picheny
2023 — conf
Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis.
IPDPS Workshops
Nicholson T. Collier, Justin M. Wozniak, Abby Stevens, Yadu N. Babuji, Mickaël Binois, Arindam Fadikar, Alexandra Würth, Kyle Chard, Jonathan Ozik
2023 J jnl
Developing Distributed High-performance Computing Capabilities of an Open Science Platform for Robust Epidemic Analysis.
CoRR
Nicholson T. Collier, Justin M. Wozniak, Abby Stevens, Yadu N. Babuji, Mickaël Binois, Arindam Fadikar, Alexandra Würth, Kyle Chard, Jonathan Ozik
2023 — conf
Improved Multi-label Propagation for Small Data with Multi-objective Optimization.
ECML/PKDD (4)
Khadija Musayeva, Mickaël Binois
2023 Misc conf
Trajectory-Oriented Optimization of Stochastic Epidemiological Models.
WSC
Arindam Fadikar, Nicholson T. Collier, Abby Stevens, Jonathan Ozik, Mickaël Binois, Kok Ben Toh
2023 — conf
Validation of Calibration Strategies for Macroscopic Traffic Flow Models on Synthetic Data.
MT-ITS
Alexandra Würth, Mickaël Binois, Paola Goatin
2022 J jnl
A Survey on High-dimensional Gaussian Process Modeling with Application to Bayesian Optimization.
ACM Trans. Evol. Learn. Optim.
Mickaël Binois, Nathan Wycoff
2022 J jnl
Data-driven uncertainty quantification in macroscopic traffic flow models.
Adv. Comput. Math.
Alexandra Würth, Mickaël Binois, Paola Goatin, Simone Göttlich
2022 J jnl
Geometrically consistent aerodynamic optimization using an isogeometric Discontinuous Galerkin method.
Comput. Math. Appl.
Stefano Pezzano, Régis Duvigneau, Mickaël Binois
2022 J jnl
Sensitivity Prewarping for Local Surrogate Modeling.
Technometrics
Nathan Wycoff, Mickaël Binois, Robert B. Gramacy
2021 J jnl
A population data-driven workflow for COVID-19 modeling and learning.
Int. J. High Perform. Comput. Appl.
Jonathan Ozik, Justin M. Wozniak, Nicholson T. Collier, Charles M. Macal, Mickaël Binois
2021 J jnl
Evaluating Gaussian process metamodels and sequential designs for noisy level set estimation.
Stat. Comput.
Xiong Lyu, Mickaël Binois, Michael Ludkovski
2021 J jnl
Sensitivity Prewarping for Local Surrogate Modeling.
CoRR
Nathan Wycoff, Mickaël Binois, Robert B. Gramacy
2021 J jnl
Sequential Learning of Active Subspaces.
J. Comput. Graph. Stat.
Nathan Wycoff, Mickaël Binois, Stefan M. Wild
2021 J jnl
hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R.
J. Stat. Softw.
Mickaël Binois, Robert B. Gramacy
2020 J jnl
On the choice of the low-dimensional domain for global optimization via random embeddings.
J. Glob. Optim.
Mickaël Binois, David Ginsbourger, Olivier Roustant
2020 J jnl
The Kalai-Smorodinsky solution for many-objective Bayesian optimization.
J. Mach. Learn. Res.
Mickaël Binois, Victor Picheny, Patrick Taillandier, Abderrahmane Habbal
2019 J jnl
A Bayesian optimization approach to find Nash equilibria.
J. Glob. Optim.
Victor Picheny, Mickaël Binois, Abderrahmane Habbal
2019 J jnl
Parameter and Uncertainty Estimation for Dynamical Systems Using Surrogate Stochastic Processes.
SIAM J. Sci. Comput.
Matthias Chung, Mickaël Binois, Robert B. Gramacy, Johnathan M. Bardsley, David J. Moquin, Amanda P. Smith, Amber M. Smith
2019 J jnl
Replication or Exploration? Sequential Design for Stochastic Simulation Experiments.
Technometrics
Mickaël Binois, Jiangeng Huang, Robert B. Gramacy, Mike Ludkovski
2019 J jnl
Sequential Learning of Active Subspaces.
CoRR
Nathan Wycoff, Mickaël Binois, Stefan M. Wild
2018 J jnl
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation.
CoRR
Xiong Lyu, Mickaël Binois, Michael Ludkovski
2015 — conf
A Warped Kernel Improving Robustness in Bayesian Optimization Via Random Embeddings.
LION
Mickaël Binois, David Ginsbourger, Olivier Roustant
2015 J jnl
On the estimation of Pareto fronts from the point of view of copula theory.
Inf. Sci.
Mickaël Binois, Didier Rullière, Olivier Roustant
2015 J jnl
Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations.
Eur. J. Oper. Res.
Mickaël Binois, David Ginsbourger, Olivier Roustant