用核密度估计生成表格数据，快且省空间。
TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
- 基于核密度估计与耦合变换，无需复杂训练。
- 生成质量接近主流方法，训练时间几乎为零。
- 模型可压缩为原始数据子集，适合资源受限场景。
表格数据生成旨在生成与原始数据分布一致的新行，而不直接复制原数据。过去四年中，该领域主要依赖计算成本高昂的方法，如独热编码、变分自编码器（VAE）和扩散模型。本文提出一种新方法：通过耦合变换建模分布，并采用核密度估计（KDE），在几乎不需训练时间的情况下，达到与现有方法相当的生成精度与隐私保护效果。该方法高度可扩展，可在普通笔记本上处理远超以往最优方法的数据规模。此外，由于使用核密度估计，模型可压缩为原始数据的共核（coreset），实现生成模型中首次的极简存储。代码已公开于https://github.com/tabkde/tabkde-main。
原文摘要 · Abstract (English)
Tabular data generation considers a large table with multiple columns -- each column comprised of numerical, categorical, or sometimes ordinal values. The goal is to produce new rows for the table that replicate the distribution of rows from the original data -- without just copying those initial rows. The last 4 years have seen enormous progress on this problem, mostly using computational expensive methods that employ one-hot encoding, VAEs, and diffusion. This paper describes a new approach to the problem of tabular data generation. By employing copula transformations and modeling the distribution as a kernel density estimate we can nearly match the accuracy and leakage-avoidance achievements of the previous methods, but with almost no training time. Our method is very scalable, and can be run on data sets orders of magnitude larger than prior state-of-the-art on a simple laptop. Moreover, because we employ kernel density estimates, we can store the model as a coreset of the original data -- we believe the first for generative modeling -- and as a result, require significantly less space as well. Our code is available here: \url{https://github.com/tabkde/tabkde-main}
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