LaTable: Towards Large Tabular Models

• URL: http://arxiv.org/abs/2406.17673v1
• Date: Tue, 25 Jun 2024 16:03:50 GMT
• Title: LaTable: Towards Large Tabular Models
• Authors: Boris van Breugel, Jonathan Crabbé, Rob Davis, Mihaela van der Schaar,
• Abstract summary: Tabular generative foundation models are hard to build due to the heterogeneous feature spaces of different datasets. LaTable is a novel diffusion model that addresses these challenges and can be trained across different datasets. We find that LaTable outperforms baselines on in-distribution generation, and that finetuning LaTable can generate out-of-distribution datasets better with fewer samples.
• Score: 63.995130144110156
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tabular data is one of the most ubiquitous modalities, yet the literature on tabular generative foundation models is lagging far behind its text and vision counterparts. Creating such a model is hard, due to the heterogeneous feature spaces of different tabular datasets, tabular metadata (e.g. dataset description and feature headers), and tables lacking prior knowledge (e.g. feature order). In this work we propose LaTable: a novel tabular diffusion model that addresses these challenges and can be trained across different datasets. Through extensive experiments we find that LaTable outperforms baselines on in-distribution generation, and that finetuning LaTable can generate out-of-distribution datasets better with fewer samples. On the other hand, we explore the poor zero-shot performance of LaTable, and what it may teach us about building generative tabular foundation models with better zero- and few-shot generation capabilities.

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