Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Examples for $k$-Nearest Neighbor Classifiers Based on
  Higher-Order Voronoi Diagrams   Adversarial examples are a widely studied phenomenon in machine learning
models. While most of the attention has been focused on neural networks, other
practical models also suffer from this issue. In this work, we propose an
algorithm for evaluating the adversarial robustness of $k$-nearest neighbor
classification, i.e., finding a minimum-norm adversarial example. Diverging
from previous proposals, we take a geometric approach by performing a search
that expands outwards from a given input point. On a high level, the search
radius expands to the nearby Voronoi cells until we find a cell that classifies
differently from the input point. To scale the algorithm to a large $k$, we
introduce approximation steps that find perturbations with smaller norm,
compared to the baselines, in a variety of datasets. Furthermore, we analyze
the structural properties of a dataset where our approach outperforms the
competition.

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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
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The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
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The categories of this paper are: nucl-ex
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The categories of this paper are: physics
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The categories of this paper are: math
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th