# Gradient-based explanations for Gaussian Process regression and classification models

@article{Seitz2022GradientbasedEF, title={Gradient-based explanations for Gaussian Process regression and classification models}, author={Sarem Seitz}, journal={ArXiv}, year={2022}, volume={abs/2205.12797} }

BSTRACT Gaussian Processes (GPs) have proven themselves as a reliable and effective method in probabilistic Machine Learning. Thanks to recent and current advances, modeling complex data with GPs is becoming more and more feasible. Thus, these types of models are, nowadays, an interesting alternative to Neural and Deep Learning methods, which are arguably the current state-of-the-art in Machine Learning. For the latter, we see an increasing interest in so-called explainable approaches in…

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