Gaussian process interpolation with conformal prediction: methods and comparative analysis
This work addresses uncertainty quantification for researchers and practitioners using Gaussian processes, but it is incremental as it adapts existing conformal prediction techniques to this domain.
The paper tackles the problem of poorly calibrated prediction intervals in Gaussian process interpolation by applying conformal prediction methods, showing that these methods improve calibration without sacrificing accuracy.
This article advocates the use of conformal prediction (CP) methods for Gaussian process (GP) interpolation to enhance the calibration of prediction intervals. We begin by illustrating that using a GP model with parameters selected by maximum likelihood often results in predictions that are not optimally calibrated. CP methods can adjust the prediction intervals, leading to better uncertainty quantification while maintaining the accuracy of the underlying GP model. We compare different CP variants and introduce a novel variant based on an asymmetric score. Our numerical experiments demonstrate the effectiveness of CP methods in improving calibration without compromising accuracy. This work aims to facilitate the adoption of CP methods in the GP community.