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Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
Gaussian processes (GPs) are a Bayesian machine learning approach widely used to construct surrogate models for the uncertainty quantification of computer simulation codes in industrial applications.
MAPIE: an open-source library for distribution-free uncertainty quantification
Estimating uncertainties associated with the predictions of Machine Learning (ML) models is of crucial importance to assess their robustness and predictive power.
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