Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarially Robust Generalization Requires More Data   Machine learning models are often susceptible to adversarial perturbations of
their inputs. Even small perturbations can cause state-of-the-art classifiers
with high "standard" accuracy to produce an incorrect prediction with high
confidence. To better understand this phenomenon, we study adversarially robust
learning from the viewpoint of generalization. We show that already in a simple
natural data model, the sample complexity of robust learning can be
significantly larger than that of "standard" learning. This gap is information
theoretic and holds irrespective of the training algorithm or the model family.
We complement our theoretical results with experiments on popular image
classification datasets and show that a similar gap exists here as well. We
postulate that the difficulty of training robust classifiers stems, at least
partially, from this inherently larger sample complexity.

26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
4
The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th