Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unique properties of adversarially trained linear classifiers on
  Gaussian data   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.

24
The categories of this paper are: dg-ga
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th