kergp: Kernel laboratory. This package, created during the ReDICE consortium, has been enriched with new functionalities: categorical variables, radial kernels, optimizer choices, etc.
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lineqGPR : Gaussian Process Regression Models with Linear Inequality Constraints.
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nestedKriging : Nested kriging models for large data sets.
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specgp: Construction of kernels by the spectral approach, suitable e.g. for large datasets.
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libKriging: This is an ongoing software project, to enhance an industrial usage of OQUAIDO results. libKriging will include fast and portable implementations for GP modeling, with a wide test coverage.
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Several notebooks and vignettes have been written to ease the discovery of these packages. There are often delivered within the packages, as part of the documentation. Finally, another package, called mixgp, dedicated to Kriging models with both discrete and continuous input variables, has been developped in the first years of the Chair. It is now included in kergp.
A sampling criterion for constrained Bayesian optimization with uncertainties, R. El Amri, C. Helbert, C. Blanchet-Scalliet, R. Le Riche, to appear (2021).
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Coupling constraints in Bayesian optimization, J. Pelamatti, R. Le Riche, C. Helbert, C. Blanchet-Scalliet, to appear (2021).
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Optimization in presence of categorical inputs with latent variables, J. Cuesta-Ramirez, C. Durantin, A. Glière, G. Perrin, R. Le Riche, O. Roustant, to appear (2021).
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Gaussian Processes For Computer Experiments,
F. Bachoc, E. Contal, H. Maatouk, and D. Rullière (2017), ESAIM: Proceedings and surveys, proceedings of MAS2016 conference, 60, p. 163-179.
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Gaussian Process Modulated Cox Processes under Linear Inequality Constraints,
A. F. López-Lopera, S. John, and N. Durrande (2019), PMLR:, proceedings of AISTATS19 conference, 89, p. 1997-2006.
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