Gaussian process interpolation with conformal prediction: methods and comparative analysis
<|reference_start|>Gaussian process interpolation with conformal prediction: methods and comparative analysis: 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.<|reference_end|>
@article{pion2024gaussian,
    title={Gaussian process interpolation with conformal prediction: methods and
  comparative analysis},
    author={Aur'elien Pion, Emmanuel Vazquez},
    journal={LOD 2024, 10th International Conference on Machine Learning,
  Optimization, and Data Science, Sep 2024, Castiglione della Pescaia Grosseto
  Italy, Italy},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08271},
    primaryClass={cs.LG stat.CO stat.ME stat.ML}
}