Recent breakthroughs in large language modeling have facilitated rigorous exploration of
their application in diverse tasks related to tabular data modeling, such as prediction, tabular …

Data augmentation is a series of techniques that generate high-quality artificial data by
manipulating existing data samples. By leveraging data augmentation techniques, AI …

Recent advances in tabular data generation have greatly enhanced synthetic data quality.
However, extending diffusion models to tabular data is challenging due to the intricately …

Accessible high-quality data is the bread and butter of machine learning research, and the
demand for data has exploded as larger and more advanced ML models are built across …

Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …

Recent research in tabular data synthesis has focused on single tables, whereas real-world
applications often involve complex data with tens or hundreds of interconnected tables …

In this study, we explore the growing potential of AI and deep learning technologies,
particularly Generative Adversarial Networks (GANs) and Large Language Models (LLMs) …

Synthetic data is becoming an increasingly promising technology, and successful
applications can improve privacy, fairness, and data democratization. While there are many …

Deep Generative Models (DGMs) have been shown to be powerful tools for generating
tabular data, as they have been increasingly able to capture the complex distributions that …

Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …