Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Robustifying Binary Classification to Adversarial Perturbation   Despite the enormous success of machine learning models in various
applications, most of these models lack resilience to (even small)
perturbations in their input data. Hence, new methods to robustify machine
learning models seem very essential. To this end, in this paper we consider the
problem of binary classification with adversarial perturbations. Investigating
the solution to a min-max optimization (which considers the worst-case loss in
the presence of adversarial perturbations) we introduce a generalization to the
max-margin classifier which takes into account the power of the adversary in
manipulating the data. We refer to this classifier as the "Robust Max-margin"
(RM) classifier. Under some mild assumptions on the loss function, we
theoretically show that the gradient descent iterates (with sufficiently small
step size) converge to the RM classifier in its direction. Therefore, the RM
classifier can be studied to compute various performance measures (e.g.
generalization error) of binary classification with adversarial perturbations.

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The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
3
The categories of this paper are: physics
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci