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Workshop: Table Representation Learning Workshop

Training-Free Generalization on Heterogeneous Tabular Data via Meta-Representation

Han-Jia Ye · Qile Zhou · De-Chuan Zhan

Keywords: [ Deep tabular learning ] [ tabular data ] [ Training-free generalization ] [ Tabular data pre-training ]

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Fri 15 Dec 9:54 a.m. PST — 10:01 a.m. PST
presentation: Table Representation Learning Workshop
Fri 15 Dec 6:30 a.m. PST — 3:30 p.m. PST


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.

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