Recently, the topic of table pre-training has attracted considerable research
interest. However, how to employ table pre-training to boost the performance of
tabular prediction remains an open challenge. In this paper, we propose TapTap,
the first attempt that leverages table pre-training to empower models for
tabular prediction. After pre-training on a large corpus of real-world tabular
data, TapTap can generate high-quality synthetic tables to support various
applications on tabular data, including privacy protection, low resource
regime, missing value imputation, and imbalanced classification. Extensive
experiments on 12 datasets demonstrate that TapTap outperforms a total of 16
baselines in different scenarios. Meanwhile, it can be easily combined with
various backbone models, including LightGBM, Multilayer Perceptron (MLP) and
Transformer. Moreover, with the aid of table pre-training, models trained using
synthetic data generated by TapTap can even compete with models using the
original dataset on half of the experimental datasets, marking a milestone in
the development of synthetic tabular data generation. The codes are available
at this https URL

该论文提出了 TapTap，利用表格预训练生成高质量的合成表格，支持各种基于表格数据的应用，通过广泛的实验验证其在 12 个数据集上的性能大大优于 16 种基线，并可轻松与各种基础模型相结合，是合成表格数据生成发展中的一个里程碑。

生成式表格预训练加强了表格预测模型

Generative Table Pre-training Empowers Models for Tabular Prediction

Generative adversarial networks (GANs) implicitly learn the probability
distribution of a dataset and can draw samples from the distribution. This
paper presents, Tabular GAN (TGAN), a generative adversarial network which can
generate tabular data like medical or educational records. Using the power of
deep neural networks, TGAN generates high-quality and fully synthetic tables
while simultaneously generating discrete and continuous variables. When we
evaluate our model on three datasets, we find that TGAN outperforms
conventional statistical generative models in both capturing the correlation
between columns and scaling up for large datasets.

本文介绍了 Tabular GAN（TGAN），这是一种可以生成表格数据（例如医学或教育记录）的生成对抗网络，利用深度神经网络生成高质量和完全合成的表格，同时生成离散和连续变量。在三个数据集上评估我们的模型时，我们发现 TGAN 在捕捉列之间的相关性和扩展大型数据集方面都优于传统的统计生成模型。