Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Varying-coefficient models with isotropic Gaussian process priors   We study learning problems in which the conditional distribution of the
output given the input varies as a function of additional task variables. In
varying-coefficient models with Gaussian process priors, a Gaussian process
generates the functional relationship between the task variables and the
parameters of this conditional. Varying-coefficient models subsume hierarchical
Bayesian multitask models, but also generalizations in which the conditional
varies continuously, for instance, in time or space. However, Bayesian
inference in varying-coefficient models is generally intractable. We show that
inference for varying-coefficient models with isotropic Gaussian process priors
resolves to standard inference for a Gaussian process that can be solved
efficiently. MAP inference in this model resolves to multitask learning using
task and instance kernels, and inference for hierarchical Bayesian multitask
models can be carried out efficiently using graph-Laplacian kernels. We report
on experiments for geospatial prediction.

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The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph