Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discriminative Nonparametric Latent Feature Relational Models with Data
  Augmentation   We present a discriminative nonparametric latent feature relational model
(LFRM) for link prediction to automatically infer the dimensionality of latent
features. Under the generic RegBayes (regularized Bayesian inference)
framework, we handily incorporate the prediction loss with probabilistic
inference of a Bayesian model; set distinct regularization parameters for
different types of links to handle the imbalance issue in real networks; and
unify the analysis of both the smooth logistic log-loss and the piecewise
linear hinge loss. For the nonconjugate posterior inference, we present a
simple Gibbs sampler via data augmentation, without making restricting
assumptions as done in variational methods. We further develop an approximate
sampler using stochastic gradient Langevin dynamics to handle large networks
with hundreds of thousands of entities and millions of links, orders of
magnitude larger than what existing LFRM models can process. Extensive studies
on various real networks show promising performance.

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The categories of this paper are: physics
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The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
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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-lat
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
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The categories of this paper are: comp-gas