Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bayesian Semi-supervised Learning with Graph Gaussian Processes   We propose a data-efficient Gaussian process-based Bayesian approach to the
semi-supervised learning problem on graphs. The proposed model shows extremely
competitive performance when compared to the state-of-the-art graph neural
networks on semi-supervised learning benchmark experiments, and outperforms the
neural networks in active learning experiments where labels are scarce.
Furthermore, the model does not require a validation data set for early
stopping to control over-fitting. Our model can be viewed as an instance of
empirical distribution regression weighted locally by network connectivity. We
further motivate the intuitive construction of the model with a Bayesian linear
model interpretation where the node features are filtered by an operator
related to the graph Laplacian. The method can be easily implemented by
adapting off-the-shelf scalable variational inference algorithms for Gaussian
processes.

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The categories of this paper are: math
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The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
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The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: q-bio