Advances in deep generative modelling have not translated well to tabular
data. We argue that this is caused by a mismatch in structure between popular
generative models and discriminative models of tabular data. We thus devise a
technique to turn TabPFN -- a highly performant transformer initially designed
for in-context discriminative tabular tasks -- into an energy-based generative
model, which we dub TabPFGen. This novel framework leverages the pre-trained
TabPFN as part of the energy function and does not require any additional
training or hyperparameter tuning, thus inheriting TabPFN's in-context learning
capability. We can sample from TabPFGen analogously to other energy-based
models. We demonstrate strong results on standard generative modelling tasks,
including data augmentation, class-balancing, and imputation, unlocking a new
frontier of tabular data generation.

通过将 TabPFN 转化为基于能量的生成模型 TabPFGen，利用其在上下文判别表格任务中的学习能力，我们在标准生成建模任务中展示出了强大的结果，包括数据增强、类别平衡和数据插补，从而开启了表格数据生成的新领域。