Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

This review article explores how emerging generative artificial intelligence (GenAI) models,
such as large language models (LLMs), can enhance solution methodologies within process …

With the increasing importance and complexity of data pipelines, data quality became one of
the key challenges in modern software applications. The importance of data quality has …

Consider the problem of imputing missing values in a dataset. One the one hand,
conventional approaches using iterative imputation benefit from the simplicity and …

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to
train models with static feature subsets. Here, we consider the dynamic feature selection …

Recently, the topic of table pre-training has attracted considerable research interest.
However, how to employ table pre-training to boost the performance of tabular prediction …

Real-world datasets often have missing values associated with complex generative
processes, where the cause of the missingness may not be fully observed. This is known as …

The diffusion model has shown remarkable performance in modeling data distributions and
synthesizing data. However, the vanilla diffusion model requires complete or fully observed …

Abstract We validated a Deep Embedded Clustering (DEC) model and its adaptation for
integrating mixed datatypes (in this study, numerical and categorical variables). Deep …

Diffusion models have emerged as a robust framework for various generative tasks,
including tabular data synthesis. However, current tabular diffusion models tend to inherit …