@inproceedings{
ma2023tabpfgen,
title={Tab{PFG}en {\textendash} Tabular Data Generation with Tab{PFN}},
author={Junwei Ma and Apoorv Dankar and George Stein and Guangwei Yu and Anthony Caterini},
booktitle={NeurIPS 2023 Second Table Representation Learning Workshop},
year={2023},
url={https://openreview.net/forum?id=4MkkNsAEmO}
}
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.
TabPFGen – Tabular Data Generation with TabPFN
Junwei Ma Apoorv Dankar George Stein Guangwei Yu Anthony Caterini