Denoising diffusion probabilistic models are becoming the leading generative modeling
paradigm for many important data modalities. Being the most prevalent in the computer …

Abstract Autoregressive and Masked Transformers are incredibly effective as generative
models and classifiers. While these models are most prevalent in NLP, they also exhibit …

Tabular medical datasets, like electronic health records (EHRs), biobanks, and structured
clinical trial data, are rich sources of information with the potential to advance precision …

Tabular data is a common form of organizing data. Multiple models are available to generate
synthetic tabular datasets where observations are independent, but few have the ability to …

Tabular data is hard to acquire and is subject to missing values. This paper introduces a
novel approach for generating and imputing mixed-type (continuous and categorical) tabular …

Background: Data sharing in healthcare is vital for advancing research and personalized
medicine. However, the process is hindered by privacy, ethical, and legal challenges …

The conglomerate reservoirs in the Mahu Sag of the Junggar Basin, northeastern China are
featured high heterogeneity and complicated lithology. The reservoirs have experienced a …

Clinical research relies on high-quality patient data, however, obtaining big data sets is
costly and access to existing data is often hindered by privacy and regulatory concerns …

Synthetic tabular data generation becomes crucial when real data are limited, expensive to
collect, or simply cannot be used due to privacy concerns. However, producing good quality …

Large language models (LLMs) have demonstrated remarkable in-context learning
capabilities across diverse applications. In this work, we explore the effectiveness of LLMs …