Continuous Diffusion for Mixed-Type Tabular Data

• URL: http://arxiv.org/abs/2312.10431v3
• Date: Mon, 30 Sep 2024 13:45:03 GMT
• Title: Continuous Diffusion for Mixed-Type Tabular Data
• Authors: Markus Mueller, Kathrin Gruber, Dennis Fok,
• Abstract summary: We propose CDTD, a Continuous Diffusion model for mixed-type Tabular Data. We counteract the high heterogeneity inherent to data of mixed-type with distinct, adaptive noise schedules. Our experimental results show that CDTD consistently outperforms state-of-the-art benchmark models.
• Score: 2.7992435001846827
• License:
• Abstract: Score-based generative models (or diffusion models for short) have proven successful for generating text and image data. However, the adaption of this model family to tabular data of mixed-type has fallen short so far. In this paper, we propose CDTD, a Continuous Diffusion model for mixed-type Tabular Data. Specifically, we combine score matching and score interpolation to ensure a common continuous noise distribution for both continuous and categorical features alike. We counteract the high heterogeneity inherent to data of mixed-type with distinct, adaptive noise schedules per feature or per data type. The learnable noise schedules ensure optimally allocated model capacity and balanced generative capability. We homogenize the data types further with model-specific loss calibration and initialization schemes tailored to mixed-type tabular data. Our experimental results show that CDTD consistently outperforms state-of-the-art benchmark models, captures feature correlations exceptionally well, and that heterogeneity in the noise schedule design boosts the sample quality.

Related papers

• 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)
• 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)
• Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models [14.651592234678722]
  Current diffusion models tend to inherit bias in the training dataset and generate biased synthetic data. We introduce a novel model that incorporates sensitive guidance to generate fair synthetic data with balanced joint distributions of the target label and sensitive attributes. Our method effectively mitigates bias in training data while maintaining the quality of the generated samples.
  arXiv  Detail & Related papers  (2024-04-12T06:08:43Z)
• Label-Noise Robust Diffusion Models [18.82847557713331]
  Conditional diffusion models have shown remarkable performance in various generative tasks. Training them requires large-scale datasets that often contain noise in conditional inputs, a.k.a. noisy labels. This paper proposes Transition-aware weighted Denoising Score Matching for training conditional diffusion models with noisy labels.
  arXiv  Detail & Related papers  (2024-02-27T14:00:34Z)
• 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)
• ChiroDiff: Modelling chirographic data with Diffusion Models [132.5223191478268]
  We introduce a powerful model-class namely "Denoising Diffusion Probabilistic Models" or DDPMs for chirographic data. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate.
  arXiv  Detail & Related papers  (2023-04-07T15:17:48Z)
• Synthesizing Mixed-type Electronic Health Records using Diffusion Models [10.973115905786129]
  Synthetic data generation is a promising solution to mitigate privacy concerns when sharing sensitive patient information. Recent studies have shown that diffusion models offer several advantages over GANs, such as generation of more realistic synthetic data and stable training in generating data modalities, including image, text, and sound. Our experiments demonstrate that TabDDPM outperforms the state-of-the-art models across all evaluation metrics, except for privacy, which confirms the trade-off between privacy and utility.
  arXiv  Detail & Related papers  (2023-02-28T15:42:30Z)
• Score-based Continuous-time Discrete Diffusion Models [102.65769839899315]
  We extend diffusion models to discrete variables by introducing a Markov jump process where the reverse process denoises via a continuous-time Markov chain. We show that an unbiased estimator can be obtained via simple matching the conditional marginal distributions. We demonstrate the effectiveness of the proposed method on a set of synthetic and real-world music and image benchmarks.
  arXiv  Detail & Related papers  (2022-11-30T05:33:29Z)
• Autoregressive Score Matching [113.4502004812927]
  We propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariable log-conditionals (scores) For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
  arXiv  Detail & Related papers  (2020-10-24T07:01:24Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)

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.