Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: How Well Can Generative Adversarial Networks Learn Densities: A
  Nonparametric View   We study in this paper the rate of convergence for learning densities under
the Generative Adversarial Networks (GAN) framework, borrowing insights from
nonparametric statistics. We introduce an improved GAN estimator that achieves
a faster rate, through simultaneously leveraging the level of smoothness in the
target density and the evaluation metric, which in theory remedies the mode
collapse problem reported in the literature. A minimax lower bound is
constructed to show that when the dimension is large, the exponent in the rate
for the new GAN estimator is near optimal. One can view our results as
answering in a quantitative way how well GAN learns a wide range of densities
with different smoothness properties, under a hierarchy of evaluation metrics.
As a byproduct, we also obtain improved generalization bounds for GAN with
deeper ReLU discriminator network.

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The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
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The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: physics
34
The categories of this paper are: chem-ph