Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Single Index Latent Variable Models for Network Topology Inference   A semi-parametric, non-linear regression model in the presence of latent
variables is applied towards learning network graph structure. These latent
variables can correspond to unmodeled phenomena or unmeasured agents in a
complex system of interacting entities. This formulation jointly estimates
non-linearities in the underlying data generation, the direct interactions
between measured entities, and the indirect effects of unmeasured processes on
the observed data. The learning is posed as regularized empirical risk
minimization. Details of the algorithm for learning the model are outlined.
Experiments demonstrate the performance of the learned model on real data.

5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph