Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data

• URL: http://arxiv.org/abs/2410.21523v1
• Date: Mon, 28 Oct 2024 20:49:26 GMT
• Title: Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data
• Authors: Hengrui Zhang, Liancheng Fang, Qitian Wu, Philip S. Yu,
• Abstract summary: This paper proposes a Diffusion-nested Autoregressive model (TabDAR) to address these issues. We conduct extensive experiments on ten datasets with distinct properties, and the proposed TabDAR outperforms previous state-of-the-art methods by 18% to 45% on eight metrics across three distinct aspects.
• Score: 56.48119008663155
• License:
• Abstract: Autoregressive models are predominant in natural language generation, while their application in tabular data remains underexplored. We posit that this can be attributed to two factors: 1) tabular data contains heterogeneous data type, while the autoregressive model is primarily designed to model discrete-valued data; 2) tabular data is column permutation-invariant, requiring a generation model to generate columns in arbitrary order. This paper proposes a Diffusion-nested Autoregressive model (TabDAR) to address these issues. To enable autoregressive methods for continuous columns, TabDAR employs a diffusion model to parameterize the conditional distribution of continuous features. To ensure arbitrary generation order, TabDAR resorts to masked transformers with bi-directional attention, which simulate various permutations of column order, hence enabling it to learn the conditional distribution of a target column given an arbitrary combination of other columns. These designs enable TabDAR to not only freely handle heterogeneous tabular data but also support convenient and flexible unconditional/conditional sampling. We conduct extensive experiments on ten datasets with distinct properties, and the proposed TabDAR outperforms previous state-of-the-art methods by 18% to 45% on eight metrics across three distinct aspects.

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