Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GANs with Conditional Independence Graphs: On Subadditivity of
  Probability Divergences   Generative Adversarial Networks (GANs) are modern methods to learn the
underlying distribution of a data set. GANs have been widely used in sample
synthesis, de-noising, domain transfer, etc. GANs, however, are designed in a
model-free fashion where no additional information about the underlying
distribution is available. In many applications, however, practitioners have
access to the underlying independence graph of the variables, either as a
Bayesian network or a Markov Random Field (MRF). We ask: how can one use this
additional information in designing model-based GANs? In this paper, we provide
theoretical foundations to answer this question by studying subadditivity
properties of probability divergences, which establish upper bounds on the
distance between two high-dimensional distributions by the sum of distances
between their marginals over (local) neighborhoods of the graphical structure
of the Bayes-net or the MRF. We prove that several popular probability
divergences satisfy some notion of subadditivity under mild conditions. These
results lead to a principled design of a model-based GAN that uses a set of
simple discriminators on the neighborhoods of the Bayes-net/MRF, rather than a
giant discriminator on the entire network, providing significant statistical
and computational benefits. Our experiments on synthetic and real-world
datasets demonstrate the benefits of our principled design of model-based GANs.

31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
20
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
29
The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: nlin
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas