  Generative Foundation Models (GFMs) have produced synthetic data with
remarkable quality in modalities such as images and text. However, applying
GFMs to tabular data poses significant challenges due to the inherent
heterogeneity of table features. Existing cross-table learning frameworks are
hindered by the absence of both a generative model backbone and a decoding
mechanism for heterogeneous feature values. To overcome these limitations, we
introduce the Cross-Table Synthesizer (CTSyn), a diffusion-based foundational
model tailored for tabular data generation. CTSyn introduces three major
components: an aggregator that consolidates heterogeneous tables into a unified
latent space; a conditional latent diffusion model for sampling from this
space; and type-specific decoders that reconstruct values of varied data types
from sampled latent vectors. Extensive testing on real-world datasets reveals
that CTSyn not only significantly outperforms existing table synthesizers in
utility and diversity, but also uniquely enhances performances of downstream
machine learning beyond what is achievable with real data, thus establishing a
new paradigm for synthetic data generation.
