Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Entropy Regularized Optimal Transport Independence Criterion   We introduce an independence criterion based on entropy regularized optimal
transport. Our criterion can be used to test for independence between two
samples. We establish non-asymptotic bounds for our test statistic and study
its statistical behavior under both the null hypothesis and the alternative
hypothesis. The theoretical results involve tools from U-process theory and
optimal transport theory. We also offer a random feature type approximation for
large-scale problems, as well as a differentiable program implementation for
deep learning applications. We present experimental results on existing
benchmarks for independence testing, illustrating the interest of the proposed
criterion to capture both linear and nonlinear dependencies in synthetic data
and real data.

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The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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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: hep-ex
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The categories of this paper are: funct-an
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The categories of this paper are: nlin