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Autor:
An, Seunghwan, Woo, Gyeongdong, Lim, Jaesung, Kim, ChangHyun, Hong, Sungchul, Jeon, Jong-June
In this paper, our goal is to generate synthetic data for heterogeneous (mixed-type) tabular datasets with high machine learning utility (MLu). Since the MLu performance depends on accurately approximating the conditional distributions, we focus on d
Externí odkaz:
http://arxiv.org/abs/2405.20602
