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cs.LG2026
Beyond Distribution Matching: Semantics-Consistent Tabular Diffusion with Weak Semantic Priors
Yili Wang, Ruxue Shi, Mengnan Du +3
Synthetic tabular data can match real data distributions while still violating the semantic constraints that govern valid tabular rows. This reveals a key limitation of existing ta…
cs.LG2026
TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning
Ruxue Shi, Yili Wang, Mengnan Du +3
Few-shot tabular learning provides a cost-effective approach for real-world applications where annotation is costly and collecting sufficient samples for new tasks is difficult. Ex…
cs.LG2025★ 3 cited
A Comprehensive Survey of Synthetic Tabular Data Generation
Ruxue Shi, Yili Wang, Mengnan Du +3
Tabular data is one of the most prevalent and important data formats in real-world applications such as healthcare, finance, and education. However, its effective use in machine le…