TabDDPM: Modelling Tabular Data with Diffusion Models
This work addresses the problem of accurate generative modeling for tabular data, which is crucial for applications like data augmentation and privacy, but it is incremental as it adapts diffusion models from other domains to tabular settings.
The authors tackled the challenge of generative modeling for heterogeneous tabular data by introducing TabDDPM, a diffusion model that universally handles any feature type, and demonstrated its superiority over GAN/VAE alternatives on benchmarks.
Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have also recently gained some attention in other domains, including speech, NLP, and graph-like data. In this work, we investigate if the framework of diffusion models can be advantageous for general tabular problems, where datapoints are typically represented by vectors of heterogeneous features. The inherent heterogeneity of tabular data makes it quite challenging for accurate modeling, since the individual features can be of completely different nature, i.e., some of them can be continuous and some of them can be discrete. To address such data types, we introduce TabDDPM -- a diffusion model that can be universally applied to any tabular dataset and handles any type of feature. We extensively evaluate TabDDPM on a wide set of benchmarks and demonstrate its superiority over existing GAN/VAE alternatives, which is consistent with the advantage of diffusion models in other fields. Additionally, we show that TabDDPM is eligible for privacy-oriented setups, where the original datapoints cannot be publicly shared.