Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gaussian Process Regression and Classification under Mathematical
  Constraints with Learning Guarantees   We introduce constrained Gaussian process (CGP), a Gaussian process model for
random functions that allows easy placement of mathematical constrains (e.g.,
non-negativity, monotonicity, etc) on its sample functions. CGP comes with
closed-form probability density function (PDF), and has the attractive feature
that its posterior distributions for regression and classification are again
CGPs with closed-form expressions. Furthermore, we show that CGP inherents the
optimal theoretical properties of the Gaussian process, e.g. rates of posterior
contraction, due to the fact that CGP is an Gaussian process with a more
efficient model space.

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The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: eess
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin