Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Gaussian Processes for Characterizing Multidimensional Change
  Surfaces   We present a scalable Gaussian process model for identifying and
characterizing smooth multidimensional changepoints, and automatically learning
changes in expressive covariance structure. We use Random Kitchen Sink features
to flexibly define a change surface in combination with expressive spectral
mixture kernels to capture the complex statistical structure. Finally, through
the use of novel methods for additive non-separable kernels, we can scale the
model to large datasets. We demonstrate the model on numerical and real world
data, including a large spatio-temporal disease dataset where we identify
previously unknown heterogeneous changes in space and time.

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The categories of this paper are: chem-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ph
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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: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th