Tabular data is prevalent in many critical domains, yet it is often
challenging to acquire in large quantities. This scarcity usually results in
poor performance of machine learning models on such data. Data augmentation, a
common strategy for performance improvement in vision and language tasks,
typically underperforms for tabular data due to the lack of explicit symmetries
in the input space. To overcome this challenge, we introduce TabMDA, a novel
method for manifold data augmentation on tabular data. This method utilises a
pre-trained in-context model, such as TabPFN, to map the data into a manifold
space. TabMDA performs label-invariant transformations by encoding the data
multiple times with varied contexts. This process explores the manifold of the
underlying in-context models, thereby enlarging the training dataset. TabMDA is
a training-free method, making it applicable to any classifier. We evaluate
TabMDA on five standard classifiers and observe significant performance
improvements across various tabular datasets. Our results demonstrate that
TabMDA provides an effective way to leverage information from pre-trained
in-context models to enhance the performance of downstream classifiers.

TabMDA 是一种用于表格数据的流形数据增强方法，利用预训练的上下文模型将数据映射到流形空间，并通过多次编码数据进行标签无关的转换，从而拓展训练数据集，为下游分类器的性能提供了一种有效的利用预训练上下文模型的方式。