arXiv:2610.15699v1 Announce Type: cross Abstract: Generative modeling for tabular data has advanced to the point where models can synthesize realistic records, recover missing values, and generate sa...
arXiv:2605.09424v2 Announce Type: replace Abstract: Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modali...
arXiv:2608. 07294v1 Announce Type: cross Abstract: Generating mixed-type tabular data requires jointly modeling diverse feature distributions and their complex cross-column dependencies.
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:2608. 14496v1 Announce Type: cross Abstract: Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets.
arXiv:2602. 07875v3 Announce Type: replace Abstract: Generating tabular data under conditions is critical to applications requiring precise control over the generative process.
The paper introduces a causal‑penalized diffusion model for generating synthetic tabular data, arguing that high statistical fidelity does not guarantee accurate causal inference. It defines causal fidelity as the discrepancy between inferential distributions from real and synthetic data and proposes a training framework that adds a causal discrepancy penalty to the generative objective. Experiments on treatment‑effect simulations and benchmark datasets demonstrate that the method can improve causal fidelity while maintaining competitive statistical fidelity.
arXiv:2610.16990v1 Announce Type: cross Abstract: Tabular data generation aims to synthesize realistic samples from limited observations while faithfully capturing the underlying data distribution. T...
arXiv:2606. 09257v1 Announce Type: cross Abstract: High-Dimensional Low-Sample Size (HDLSS) tabular domains (e.
arXiv:2609.16069v1 Announce Type: cross Abstract: Synthetic tabular data can match real data distributions while still violating the semantic constraints that govern valid tabular rows. This reveals...
arXiv:2606. 03347v1 Announce Type: cross Abstract: Score-based diffusion models have emerged as prominent deep generative models; however, their application to tabular data remains challenging because their backbones assume fully specified inputs, whereas real-world tabular data often contain missing values.
arXiv:2603. 23016v2 Announce Type: replace-cross Abstract: Tabular data is more challenging to generate than text and images, due to its heterogeneous features and much lower sample sizes.