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• W3033258370 abstract "Machine learning models are vulnerable to adversarial perturbations, that when added to an input, can cause high confidence misclassifications. The adversarial learning research community has made remarkable progress in the understanding of the root causes of adversarial perturbations. However, most problems that one may consider important to solve for the deployment of machine learning in safety critical tasks involve high dimensional complex manifolds that are difficult to characterize and study. It is common to develop adversarially robust learning theory on simple problems, in the hope that insights will transfer to `real world datasets'. In this work, we discuss a setting where this approach fails. In particular, we show with a linear classifier, it is always possible to solve a binary classification problem on Gaussian data under arbitrary levels of adversarial corruption during training, and that this property is not observed with non-linear classifiers on the CIFAR-10 dataset." @default.
• W3033258370 created "2020-06-12" @default.
• W3033258370 creator A5020715720 @default.
• W3033258370 date "2020-06-06" @default.
• W3033258370 modified "2023-09-27" @default.
• W3033258370 title "Unique properties of adversarially trained linear classifiers on Gaussian data." @default.
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• W3033258370 hasPublicationYear "2020" @default.
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