Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Is Ordered Weighted $\ell_1$ Regularized Regression Robust to
  Adversarial Perturbation? A Case Study on OSCAR   Many state-of-the-art machine learning models such as deep neural networks
have recently shown to be vulnerable to adversarial perturbations, especially
in classification tasks. Motivated by adversarial machine learning, in this
paper we investigate the robustness of sparse regression models with strongly
correlated covariates to adversarially designed measurement noises.
Specifically, we consider the family of ordered weighted $\ell_1$ (OWL)
regularized regression methods and study the case of OSCAR (octagonal shrinkage
clustering algorithm for regression) in the adversarial setting. Under a
norm-bounded threat model, we formulate the process of finding a maximally
disruptive noise for OWL-regularized regression as an optimization problem and
illustrate the steps towards finding such a noise in the case of OSCAR.
Experimental results demonstrate that the regression performance of grouping
strongly correlated features can be severely degraded under our adversarial
setting, even when the noise budget is significantly smaller than the
ground-truth signals.

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The categories of this paper are: chem-ph
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The categories of this paper are: math
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: hep-ph
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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an