arXiv AI
Beyond Distribution Matching: Semantics-Consistent Tabular Diffusion with Weak Semantic Priors
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arXiv AI
Jun 26
Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes
arXiv:2509. 09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-quality tabular data is insufficient.
arXiv Machine Learning
1d ago
CDMD: A Cross-Dataset Mixed-Type Diffusion Model for Tabular Data
arXiv:2609.39124v1 Announce Type: new Abstract: Generative models for tabular data are typically trained separately for each dataset, limiting knowledge transfer and requiring the storage of many spe...
arXiv Machine Learning
Jul 15
Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework
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.
arXiv AI
Aug 5
Enhancing Tabular Learners with Context-Aware Semantic Embeddings
arXiv:2608. 03565v1 Announce Type: new Abstract: While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discrete symbols, ignoring the rich semantics inherent in feature names or cell entries.
arXiv Machine Learning
Jun 2
Segment-driven Structural Induction and Semantic Alignment for Heterogeneous Tabular Representation
arXiv:2606. 01890v1 Announce Type: new Abstract: Real-world domains often contain heterogeneous tables whose headers vary while their underlying attribute semantics are shared, making it difficult to induce domain-specialized semantics from table-local evidence alone.
arXiv AI
Sep 10
From Synthetic Priors to Model Behavior: Structural Coverage in Tabular Foundation Models
The paper investigates how synthetic pretraining priors used in tabular foundation models (TFMs) influence downstream performance. By reconstructing the synthetic data generators of four TFMs and comparing their generated tasks to two popular tabular benchmarks using structural descriptors, the authors measure structural coverage and normalized density. They find that some generators provide broader and denser support for benchmark tasks, and that stronger synthetic-to-benchmark support generally correlates with better model performance.