Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular
  Data Synthesis   Tabular data synthesis has received wide attention in the literature. This is
because available data is often limited, incomplete, or cannot be obtained
easily, and data privacy is becoming increasingly important. In this work, we
present a generalized GAN framework for tabular synthesis, which combines the
adversarial training of GANs and the negative log-density regularization of
invertible neural networks. The proposed framework can be used for two
distinctive objectives. First, we can further improve the synthesis quality, by
decreasing the negative log-density of real records in the process of
adversarial training. On the other hand, by increasing the negative log-density
of real records, realistic fake records can be synthesized in a way that they
are not too much close to real records and reduce the chance of potential
information leakage. We conduct experiments with real-world datasets for
classification, regression, and privacy attacks. In general, the proposed
method demonstrates the best synthesis quality (in terms of task-oriented
evaluation metrics, e.g., F1) when decreasing the negative log-density during
the adversarial training. If increasing the negative log-density, our
experimental results show that the distance between real and fake records
increases, enhancing robustness against privacy attacks.

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The categories of this paper are: q-alg
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
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The categories of this paper are: math
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph