论文标题
从表格数据中生成合成样品
Generate synthetic samples from tabular data
论文作者
论文摘要
从数据集中生成新样本可以减轻额外昂贵的操作，增加的入侵程序并减轻隐私问题。当关注隐私时，这些新颖的样本在统计上是稳定的，可以用作临时和中间的替代品。此方法可以实现更好的数据共享实践，而没有与识别问题或偏见有关的对抗性攻击缺陷的问题。
Generating new samples from data sets can mitigate extra expensive operations, increased invasive procedures, and mitigate privacy issues. These novel samples that are statistically robust can be used as a temporary and intermediate replacement when privacy is a concern. This method can enable better data sharing practices without problems relating to identification issues or biases that are flaws for an adversarial attack.
