TabDDPM: Modelling Tabular Data with Diffusion Models

Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, Artem Babenko
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:17564-17579, 2023.

Abstract

Denoising diffusion probabilistic models are becoming the leading generative modeling paradigm for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have 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 data points 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 a completely different nature, i.e., some of them can be continuous and some 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 feature types. 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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-kotelnikov23a, title = {{T}ab{DDPM}: Modelling Tabular Data with Diffusion Models}, author = {Kotelnikov, Akim and Baranchuk, Dmitry and Rubachev, Ivan and Babenko, Artem}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {17564--17579}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/kotelnikov23a/kotelnikov23a.pdf}, url = {https://proceedings.mlr.press/v202/kotelnikov23a.html}, abstract = {Denoising diffusion probabilistic models are becoming the leading generative modeling paradigm for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have 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 data points 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 a completely different nature, i.e., some of them can be continuous and some 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 feature types. 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.} }
Endnote
%0 Conference Paper %T TabDDPM: Modelling Tabular Data with Diffusion Models %A Akim Kotelnikov %A Dmitry Baranchuk %A Ivan Rubachev %A Artem Babenko %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-kotelnikov23a %I PMLR %P 17564--17579 %U https://proceedings.mlr.press/v202/kotelnikov23a.html %V 202 %X Denoising diffusion probabilistic models are becoming the leading generative modeling paradigm for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have 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 data points 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 a completely different nature, i.e., some of them can be continuous and some 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 feature types. 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.
APA
Kotelnikov, A., Baranchuk, D., Rubachev, I. & Babenko, A.. (2023). TabDDPM: Modelling Tabular Data with Diffusion Models. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:17564-17579 Available from https://proceedings.mlr.press/v202/kotelnikov23a.html.

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