Diffusion models have emerged as a robust framework for various generative
tasks, such as image and audio synthesis, and have also demonstrated a
remarkable ability to generate mixed-type tabular data comprising both
continuous and discrete variables. However, current approaches to training
diffusion models on mixed-type tabular data tend to inherit the imbalanced
distributions of features present in the training dataset, which can result in
biased sampling. In this research, we introduce a fair diffusion model designed
to generate balanced data on sensitive attributes. We present empirical
evidence demonstrating that our method effectively mitigates the class
imbalance in training data while maintaining the quality of the generated
samples. Furthermore, we provide evidence that our approach outperforms
existing methods for synthesizing tabular data in terms of performance and
fairness.

介绍了一种公平的扩散模型，用于生成平衡的敏感属性数据，并通过实证证据表明该方法在训练数据中有效地减轻了类别不平衡问题，同时保持生成样本的质量，并且在性能和公平性方面优于现有方法用于综合表格数据。

用扩散模型合成平衡的混合类型表格数据

Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models

Score-based generative models (or diffusion models for short) have proven
successful across many domains in generating text and image data. However, the
consideration of mixed-type tabular data with this model family has fallen
short so far. Existing research mainly combines different diffusion processes
without explicitly accounting for the feature heterogeneity inherent to tabular
data. In this paper, we combine score matching and score interpolation to
ensure a common type of continuous noise distribution that affects both
continuous and categorical features alike. Further, we investigate the impact
of distinct noise schedules per feature or per data type. We allow for
adaptive, learnable noise schedules to ensure optimally allocated model
capacity and balanced generative capability. Results show that our model
consistently outperforms state-of-the-art benchmark models and that accounting
for heterogeneity within the noise schedule design boosts the sample quality.

通过结合评分匹配和评分插值，我们在这篇论文中提出了一种针对混合类型表格数据的得分生成模型。模型考虑了特征异质性，并允许自适应、可学习的噪声调度以提高样本质量。实验结果表明，我们的模型在质量上始终优于最先进的基准模型，并且噪声调度设计中的异质性考虑提升了样本质量。

混合类型表格数据的连续扩散

Continuous Diffusion for Mixed-Type Tabular Data

We focus on the problem of unsupervised cell outlier detection and repair in
mixed-type tabular data. Traditional methods are concerned only with detecting
which rows in the dataset are outliers. However, identifying which cells are
corrupted in a specific row is an important problem in practice, and the very
first step towards repairing them. We introduce the Robust Variational
Autoencoder (RVAE), a deep generative model that learns the joint distribution
of the clean data while identifying the outlier cells, allowing their
imputation (repair). RVAE explicitly learns the probability of each cell being
an outlier, balancing different likelihood models in the row outlier score,
making the method suitable for outlier detection in mixed-type datasets. We
show experimentally that not only RVAE performs better than several
state-of-the-art methods in cell outlier detection and repair for tabular data,
but also that is robust against the initial hyper-parameter selection.

本文提出了 Robust Variational Autoencoder (RVAE) 作为一个深度生成模型来解决表格数据中无监督的异常点检测和修复问题，RVAE 能够识别出异常的单元格并进行修复，该方法在混合的表格数据中的异常点检测和修复中表现优异。