Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Testing and Learning on Distributions with Symmetric Noise Invariance   Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD),
the resulting distance between distributions, are useful tools for fully
nonparametric two-sample testing and learning on distributions. However, it is
rarely that all possible differences between samples are of interest --
discovered differences can be due to different types of measurement noise, data
collection artefacts or other irrelevant sources of variability. We propose
distances between distributions which encode invariance to additive symmetric
noise, aimed at testing whether the assumed true underlying processes differ.
Moreover, we construct invariant features of distributions, leading to learning
algorithms robust to the impairment of the input distributions with symmetric
additive noise.

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The categories of this paper are: plasm-ph
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin
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The categories of this paper are: cond-mat
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The categories of this paper are: physics
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The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: atom-ph