DBScholar

Back to papers

Controllable Tabular Data Synthesis Using Diffusion Models

Summary: Unconditional tabular diffusion is learned, with lightweight controllers enforcing user-defined conditions (fixed attributes, cross-table correlations). A correlation-aware sampler preserves realism under control, delivering SOTA results. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6899
Venue
SIGMOD
Year
2024
Pagerank
5.8415111e-05
Overall Rank
6,555 | 55.03%
DOI
10.1145/3639283

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{liu_sigmod24,
        title = {{Controllable Tabular Data Synthesis Using Diffusion Models}},
        author = {Liu, Tongyu and Fan, Ju and Tang, Nan and Li, Guoliang and Du, Xiaoyong},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3639283},
        url = {https://dl.acm.org/doi/10.1145/3639283},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers