TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation (2410.20626v3)

Abstract: Synthesizing high-quality tabular data is an important topic in many data science tasks, ranging from dataset augmentation to privacy protection. However, developing expressive generative models for tabular data is challenging due to its inherent heterogeneous data types, complex inter-correlations, and intricate column-wise distributions. In this paper, we introduce TabDiff, a joint diffusion framework that models all mixed-type distributions of tabular data in one model. Our key innovation is the development of a joint continuous-time diffusion process for numerical and categorical data, where we propose feature-wise learnable diffusion processes to counter the high disparity of different feature distributions. TabDiff is parameterized by a transformer handling different input types, and the entire framework can be efficiently optimized in an end-to-end fashion. We further introduce a mixed-type stochastic sampler to automatically correct the accumulated decoding error during sampling, and propose classifier-free guidance for conditional missing column value imputation. Comprehensive experiments on seven datasets demonstrate that TabDiff achieves superior average performance over existing competitive baselines across all eight metrics, with up to $22.5\%$ improvement over the state-of-the-art model on pair-wise column correlation estimations. Code is available at https://github.com/MinkaiXu/TabDiff.

• The paper presents TabDiff, which innovatively applies a continuous-time diffusion process with transformers to accurately model heterogeneous tabular data.
• The methodology utilizes feature-specific diffusion schedules and a multi-modal stochastic sampler to effectively preserve complex inter-feature relationships.
• Empirical results show up to a 22.5% improvement in pairwise correlation estimation, highlighting TabDiff's potential for robust synthetic data generation.

An Analysis of TabDiff: A Multi-Modal Diffusion Model for Tabular Data Generation

Overview

The paper introduces TabDiff, an innovative multi-modal diffusion model explicitly designed for the generation of high-quality synthetic tabular data. Addressing the unique challenges posed by tabular datasets, which include heterogeneous data types, complex inter-feature dependencies, and intricate column-wise distributions, the model leverages a joint diffusion framework to simulate the underlying data distribution effectively. By modeling both numerical and categorical features through a unified continuous-time diffusion process, TabDiff navigates the distinctiveness and interconnectedness inherent in tabular data features. The model's architecture incorporates transformers and is optimized in an end-to-end manner, enhanced by techniques like classifier-free guidance for missing data imputation.

Methodology

TabDiff's primary contribution lies in its application of a continuous-time diffusion framework, traditionally reserved for Gaussian noise processes in continuous data types, to simulate heterogeneous tabular data. The model employs feature-wise learnable diffusion processes, allowing it to allocate modeling capacity differentially across features based on their individual distributions. The model’s transformer architecture facilitates the processing of numerous data types, accommodating mixed modalities without reliance on separate encodings for each type.

The model introduces a multi-modal stochastic sampler, which improves sampling by automatically correcting accumulated errors, a frequent issue arising during the iterative reversal process in diffusion models. This stochastic sampling approach enhances the fidelity of synthesized data by ensuring outputs better reflect observed correlations and distributions.

Empirical Evaluation

The paper’s experimental results demonstrate that TabDiff achieves superior performance over existing models across several datasets and metrics. In particular, the model shows up to a 22.5% improvement in estimating pairwise column correlations. Metrics utilized in the paper, including fidelity, machine learning efficiency, and data privacy, reveal TabDiff's robustness in generating synthetic data that closely mirrors the real-world counterparts.

The novelty of TabDiff’s diffusion process, combined with its optimization strategy, is evidenced in its handling of heterogeneous features and complex inter-relationships in data. This handling results in more reliable synthetic datasets that preserve the statistical properties and feature interactions of real data, which is crucial for applications in training robust and generalizable AI models.

Implications and Future Directions

The introduction of feature-specific diffusion schedules and enhanced sampling procedures marks a significant advancement in the generation of synthetic tabular data. From a practical perspective, the ability to generate high-quality, anonymized datasets could facilitate broader data sharing and collaboration in fields constrained by privacy concerns, such as healthcare and finance.

Theoretically, TabDiff opens pathways for further exploration in multi-modal data synthesis using diffusion processes. Future work could extend TabDiff's capabilities to handle more complex data types and interdependencies, as well as apply its framework across broader AI applications requiring synthetic data. Moreover, exploration of additional guidance mechanisms and diffusion process variations could enhance both the quality and applicability of generated data.

In summary, this paper offers a substantive contribution to the field of synthetic data generation and opens avenues for further research and application in multi-modal data contexts. The demonstrated improvements in data fidelity and feature interaction capture underline TabDiff's potential impact on data science and related AI tasks.