Adversarial robustness guarantees for classification with Gaussian Processes
We investigate adversarial robustness of Gaussian Process classification (GPC) models. Specifically, given a compact subset of the input space T⊆ℝd enclosing a test point x∗ and a GPC trained on a dataset , we aim to compute the minimum and the maximum classification probability for the GPC over al...
| Main Authors: | , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
Proceedings of Machine Learning Research
2020
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