Training-Free Generalization on Heterogeneous Tabular Data via
  Meta-Representation
Han-Jia Ye Qi-Le Zhou De-Chuan Zhan
Tabular data is prevalent across various machine learning domains. Yet, the
inherent heterogeneities in attribute and class spaces across different tabular
datasets hinder the effective sharing of knowledge, limiting a tabular model to
benefit from other datasets. In this paper, we propose Tabular data
Pre-Training via Meta-representation (TabPTM), which allows one tabular model
pre-training on a set of heterogeneous datasets. Then, this pre-trained model
can be directly applied to unseen datasets that have diverse attributes and
classes without additional training. Specifically, TabPTM represents an
instance through its distance to a fixed number of prototypes, thereby
standardizing heterogeneous tabular datasets. A deep neural network is then
trained to associate these meta-representations with dataset-specific
classification confidences, endowing TabPTM with the ability of training-free
generalization. Experiments validate that TabPTM achieves promising performance
in new datasets, even under few-shot scenarios.