arXiv Machine Learning July 15, 2026 By Junfeng Nie, Alvin Jin, Xiaohui Chen

Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework

Read the original on arXiv Machine Learning →

arXiv:2605. 28198v2 Announce Type: replace Abstract: Existing approaches for synthetic tabular data generation are based on either purely generative models or LLMs, both of which struggle with data heterogeneity, logical consistency, rare-event coverage, and robustness in low-data regimes.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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