activity
20142026
most citedPractical heteroskedastic Gaussian process modeling for large simulation experiments

184 citations · 318 across the 26 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

stat.AP2024

Improving Policy-Oriented Agent-Based Modeling with History Matching: A Case Study

David O'Gara, Cliff C. Kerr, Daniel J. Klein +3

Advances in computing power and data availability have led to growing sophistication in mechanistic mathematical models of social dynamics. Increasingly these models are used to in…

math.OC2024

Gearing Gaussian process modeling and sequential design towards stochastic simulators

Mickael Binois, Arindam Fadikar, Abby Stevens

This chapter presents specific aspects of Gaussian process modeling in the presence of complex noise. Starting from the standard homoscedastic model, various generalizations from t…

math.OC2024★ 1 cited

Parametric Shape Optimization of Flagellated Micro-Swimmers Using Bayesian Techniques

Lucas Palazzolo, Laëtitia Giraldi, Mickael Binois +1

Understanding and optimizing the design of helical micro-swimmers is crucial for advancing their application in various fields. This study presents an innovative approach combining…

math.OC2024

Combining additivity and active subspaces for high-dimensional Gaussian process modeling

Mickael Binois, Victor Picheny

Gaussian processes are a widely embraced technique for regression due to their good prediction accuracy, analytical tractability, and built-in capabilities for uncertainty quantifi…

stat.ME2024

Shared active subspace for multivariate vector-valued functions

Khadija Musayeva, Mickael Binois

This paper proposes several approaches as baselines to compute a shared active subspace for multivariate vector-valued functions. The goal is to minimize the deviation between the…