Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data
  Generation   Synthetic data generation overcomes limitations of real-world machine
learning. Traditional methods are valuable for augmenting costly datasets but
only optimize one criterion: realism. In this paper, we tackle the problem of
generating synthetic data that optimize multiple criteria. This goal is
necessary when real data are replaced by synthetic for privacy preservation. We
introduce HydraGAN, a new approach to synthetic data generation that introduces
multiple generator and discriminator agents into the system. The multi-agent
GAN optimizes the goal of privacy-preservation as well as data realism. To
facilitate multi-agent training, we adapt game-theoretic principles to offer
equilibrium guarantees. We observe that HydraGAN outperforms baseline methods
for three datasets for multiple criteria of maximizing data realism, maximizing
model accuracy, and minimizing re-identification risk.

24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom