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TabDDPM: Modelling Tabular Data with Diffusion Models

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arxiv 2209.15421 v2 pith:Q6WGSB7V submitted 2022-09-30 cs.LG

Akim Kotelnikov , Dmitry Baranchuk , Ivan Rubachev , Artem Babenko This is my paper
classification cs.LG
keywords diffusiondatamodelstabddpmtabularsomedatapointsfeatures
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 65 citations worldwide. Full citation record

  1. Optimal Mixture-of-Experts Model Averaging for Conditional Generative Models

    stat.ML 2026-07 accept novelty 6.5 of 10

    Sample-based MMD model averaging of conditional generators is asymptotically optimal, and input-adaptive MoEMA weights improve over fixed averaging and single models across modalities.

  2. FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents

    cs.LG 2026-07 conditional novelty 6.0 of 10

    FairDiffuseVQVAE reaches state-of-the-art fairness on the standard tabular benchmark (DPR 0.702, EOR 0.686) by uniform protected-attribute sampling at inference, paying ~15 AUC points of utility.

  3. Ensembling Membership Inference Attacks Against Tabular Generative Models

    cs.CR 2025-09 conditional novelty 6.0 of 10

    No single membership inference attack dominates across tabular generative models, and unsupervised ensembles of attacks achieve better average rankings.

  4. Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection

    cs.LG 2025-07 conditional novelty 4.0 of 10

    A diffusion-based counterfactual explanation method for network intrusion detection, with distilled fast sampling and decision-tree global rules, is evaluated against six baselines on three NIDS datasets.

  5. Towards High Supervised Learning Utility Training Data Generation: Data Pruning and Column Reordering

    cs.LG 2025-07 reject novelty 4.0 of 10

    PRRO combines signal-based data pruning and column reordering to improve the supervised learning utility of synthetic tabular data, but its evaluation is undermined by data manipulation and an ill-defined correlation measure.

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