TabDDPM: Modelling Tabular Data with Diffusion Models

• URL: http://arxiv.org/abs/2209.15421v2
• Date: Mon, 07 Oct 2024 12:38:57 GMT
• Title: TabDDPM: Modelling Tabular Data with Diffusion Models
• Authors: Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, Artem Babenko,
• Abstract summary: We introduce TabDDPM -- a diffusion model that can be universally applied to any 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.
• Score: 33.202222842342465
• License:
• Abstract: 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.

Related papers

• Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data [56.48119008663155]
  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.
  arXiv  Detail & Related papers  (2024-10-28T20:49:26Z)
• TabDiff: a Multi-Modal Diffusion Model for Tabular Data Generation [91.50296404732902]
  We introduce TabDiff, a joint diffusion framework that models all multi-modal 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. TabDiff achieves superior average performance over existing competitive baselines, with up to $22.5%$ improvement over the state-of-the-art model on pair-wise column correlation estimations.
  arXiv  Detail & Related papers  (2024-10-27T22:58:47Z)
• Self-Supervision Improves Diffusion Models for Tabular Data Imputation [20.871219616589986]
  This paper introduces an advanced diffusion model named Self-supervised imputation Diffusion Model (SimpDM for brevity) To mitigate sensitivity to noise, we introduce a self-supervised alignment mechanism that aims to regularize the model, ensuring consistent and stable imputation predictions. We also introduce a carefully devised state-dependent data augmentation strategy within SimpDM, enhancing the robustness of the diffusion model when dealing with limited data.
  arXiv  Detail & Related papers  (2024-07-25T13:06:30Z)
• Learning Divergence Fields for Shift-Robust Graph Representations [73.11818515795761]
  In this work, we propose a geometric diffusion model with learnable divergence fields for the challenging problem with interdependent data. We derive a new learning objective through causal inference, which can guide the model to learn generalizable patterns of interdependence that are insensitive across domains.
  arXiv  Detail & Related papers  (2024-06-07T14:29:21Z)
• An improved tabular data generator with VAE-GMM integration [9.4491536689161]
  We propose a novel Variational Autoencoder (VAE)-based model that addresses limitations of current approaches. Inspired by the TVAE model, our approach incorporates a Bayesian Gaussian Mixture model (BGM) within the VAE architecture. We thoroughly validate our model on three real-world datasets with mixed data types, including two medically relevant ones.
  arXiv  Detail & Related papers  (2024-04-12T12:31:06Z)
• FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation [5.824064631226058]
  We introduce textitFederated Tabular Diffusion (FedTabDiff) for generating high-fidelity mixed-type tabular data without centralized access to the original datasets. FedTabDiff realizes a decentralized learning scheme that permits multiple entities to collaboratively train a generative model while respecting data privacy and locality. Experimental evaluations on real-world financial and medical datasets attest to the framework's capability to produce synthetic data that maintains high fidelity, utility, privacy, and coverage.
  arXiv  Detail & Related papers  (2024-01-11T21:17:50Z)
• Training-Free Generalization on Heterogeneous Tabular Data via Meta-Representation [67.30538142519067]
  We propose Tabular data Pre-Training via Meta-representation (TabPTM) A deep neural network is then trained to associate these meta-representations with dataset-specific classification confidences. Experiments validate that TabPTM achieves promising performance in new datasets, even under few-shot scenarios.
  arXiv  Detail & Related papers  (2023-10-31T18:03:54Z)
• Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution [67.9215891673174]
  We propose score entropy as a novel loss that naturally extends score matching to discrete spaces. We test our Score Entropy Discrete Diffusion models on standard language modeling tasks.
  arXiv  Detail & Related papers  (2023-10-25T17:59:12Z)
• MissDiff: Training Diffusion Models on Tabular Data with Missing Values [29.894691645801597]
  This work presents a unified and principled diffusion-based framework for learning from data with missing values. We first observe that the widely adopted "impute-then-generate" pipeline may lead to a biased learning objective. We prove the proposed method is consistent in learning the score of data distributions, and the proposed training objective serves as an upper bound for the negative likelihood in certain cases.
  arXiv  Detail & Related papers  (2023-07-02T03:49:47Z)
• T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified Visual Modalities [69.16656086708291]
  Diffusion Probabilistic Field (DPF) models the distribution of continuous functions defined over metric spaces. We propose a new model comprising of a view-wise sampling algorithm to focus on local structure learning. The model can be scaled to generate high-resolution data while unifying multiple modalities.
  arXiv  Detail & Related papers  (2023-05-24T03:32:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.