Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Two-sample Test using Projected Wasserstein Distance: Breaking the Curse
  of Dimensionality   We develop a projected Wasserstein distance for the two-sample test, a
fundamental problem in statistics and machine learning: given two sets of
samples, to determine whether they are from the same distribution. In
particular, we aim to circumvent the curse of dimensionality in Wasserstein
distance: when the dimension is high, it has diminishing testing power, which
is inherently due to the slow concentration property of Wasserstein metrics in
the high dimension space. A key contribution is to couple optimal projection to
find the low dimensional linear mapping to maximize the Wasserstein distance
between projected probability distributions. We characterize the theoretical
property of the finite-sample convergence rate on IPMs and present practical
algorithms for computing this metric. Numerical examples validate our
theoretical results.

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The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
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The categories of this paper are: q-fin