Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior   We revisit Rahimi and Recht (2007)'s kernel random Fourier features (RFF)
method through the lens of the PAC-Bayesian theory. While the primary goal of
RFF is to approximate a kernel, we look at the Fourier transform as a prior
distribution over trigonometric hypotheses. It naturally suggests learning a
posterior on these hypotheses. We derive generalization bounds that are
optimized by learning a pseudo-posterior obtained from a closed-form
expression. Based on this study, we consider two learning strategies: The first
one finds a compact landmarks-based representation of the data where each
landmark is given by a distribution-tailored similarity measure, while the
second one provides a PAC-Bayesian justification to the kernel alignment method
of Sinha and Duchi (2016).

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The categories of this paper are: adap-org
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: funct-an
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: math
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The categories of this paper are: q-fin