Adversarial robustness guarantees for Gaussian processes
Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive for safety-critical applications. Such scenarios demand that GP decisions are not only accurate, but also robust to perturbations. In this paper we present a framework to analyse adversarial robustnes...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Journal of Machine Learning Research
2022
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