Constrained Gaussian process regression: an adaptive approach for the estimation of hyperparameters and the verification of constraints with high probability
- DOI
- 10.1615/jmachlearnmodelcomput.2021039837
- Published
- 2021-09-06
- Container
- Journal of Machine Learning for Modeling and Computing
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1615/jmachlearnmodelcomput.2021039837,
title = {Constrained Gaussian process regression: an adaptive approach for the estimation of hyperparameters and the verification of constraints with high probability},
author = {Guillaume Perrin and S. da Veiga},
year = {2021},
journal = {Journal of Machine Learning for Modeling and Computing},
doi = {10.1615/jmachlearnmodelcomput.2021039837},
url = {https://doi.org/10.1615/jmachlearnmodelcomput.2021039837}
}RIS
TY - JOUR TI - Constrained Gaussian process regression: an adaptive approach for the estimation of hyperparameters and the verification of constraints with high probability AU - Guillaume Perrin AU - S. da Veiga PY - 2021 JO - Journal of Machine Learning for Modeling and Computing DO - 10.1615/jmachlearnmodelcomput.2021039837 UR - https://doi.org/10.1615/jmachlearnmodelcomput.2021039837 ER -
APA
Perrin, G., & Veiga, S. D. (2021). Constrained Gaussian process regression: an adaptive approach for the estimation of hyperparameters and the verification of constraints with high probability. Journal of Machine Learning for Modeling and Computing. https://doi.org/10.1615/jmachlearnmodelcomput.2021039837
Source records
- hal · retrieved 2026-11-10T06:49:07.465Z