TabPFGen -- Tabular Data Generation with TabPFN

• URL: http://arxiv.org/abs/2406.05216v1
• Date: Fri, 7 Jun 2024 18:59:37 GMT
• Title: TabPFGen -- Tabular Data Generation with TabPFN
• Authors: Junwei Ma, Apoorv Dankar, George Stein, Guangwei Yu, Anthony Caterini,
• Abstract summary: We turn TabPFN, a highly performant transformer, into an energy-based generative model, which we dub TabPFGen. We demonstrate strong results on standard generative modelling tasks, including data augmentation, class-balancing, and imputation.
• Score: 4.743548909570325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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