Cover Image

CTSyn: A Foundation Model for Cross Tabular Data Generation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lin, Xiaofeng, Xu, Chenheng, Yang, Matthew, Cheng, Guang
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Risk In Context: Benchmarking Privacy Leakage of Foundation Models in Synthetic Tabular Data Generation
by: Byun, Jessup, et al.
Published: (2025)

Theoretical Understanding of In-Context Learning in Shallow Transformers with Unstructured Data
by: Xing, Yue, et al.
Published: (2024)

SYNTHONY: A Stress-Aware, Intent-Conditioned Agent for Deep Tabular Generative Models Selection
by: Son, Hochan, et al.
Published: (2026)

Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
by: Ward, Joshua, et al.
Published: (2025)

ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning
by: Lin, Xiaofeng, et al.
Published: (2026)

Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models
by: Ward, Joshua, et al.
Published: (2024)

Watermarking Generative Tabular Data
by: He, Hengzhi, et al.
Published: (2024)

Tabular Foundation Model for Generative Modelling
by: Jiang, Xiangjian, et al.
Published: (2026)

Robust Tabular Foundation Models
by: Peroni, Matthew, et al.
Published: (2025)

Ensembling Membership Inference Attacks Against Tabular Generative Models
by: Ward, Joshua, et al.
Published: (2025)

UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science
by: Yang, Yazheng, et al.
Published: (2023)

Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model
by: Shao, Kaixuan, et al.
Published: (2026)

Simple and Robust Forecasting of Spatiotemporally Correlated Small Earth Data with A Tabular Foundation Model
by: Yang, Yuting, et al.
Published: (2025)

TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields
by: Arazi, Alan, et al.
Published: (2025)

Towards Benchmarking Foundation Models for Tabular Data With Text
by: Mráz, Martin, et al.
Published: (2025)

TabularFM: An Open Framework For Tabular Foundational Models
by: Tran, Quan M., et al.
Published: (2024)

Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges
by: Wu, Xiaofeng, et al.
Published: (2025)

Statistically Accurate and Robust Generative Prediction of Rock Discontinuities with A Tabular Foundation Model
by: Meng, Han, et al.
Published: (2025)

When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generation
by: Ward, Joshua, et al.
Published: (2025)

On Finetuning Tabular Foundation Models
by: Rubachev, Ivan, et al.
Published: (2025)

Turning Tabular Foundation Models into Graph Foundation Models
by: Eremeev, Dmitry, et al.
Published: (2025)

Distilling Tabular Foundation Models for Structured Health Data
by: Tanna, Aditya, et al.
Published: (2026)

A Mechanistic Study of Tabular Foundation Models
by: Biloš, Marin, et al.
Published: (2026)

Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
by: Bühler, Magnus, et al.
Published: (2026)

TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data
by: Suh, Namjoon, et al.
Published: (2024)

GReaTER: Generate Realistic Tabular data after data Enhancement and Reduction
by: Kwok, Tung Sum Thomas, et al.
Published: (2025)

TabDPT: Scaling Tabular Foundation Models on Real Data
by: Ma, Junwei, et al.
Published: (2024)

In-context Learning of Evolving Data Streams with Tabular Foundational Models
by: Lourenço, Afonso, et al.
Published: (2025)

Utility Theory of Synthetic Data Generation
by: Xu, Shirong, et al.
Published: (2023)

A Comprehensive Survey of Synthetic Tabular Data Generation
by: Shi, Ruxue, et al.
Published: (2025)

TabularQGAN: A Quantum Generative Model for Tabular Data
by: Bhardwaj, Pallavi, et al.
Published: (2025)

Real-Time Explanations for Tabular Foundation Models
by: Sena, Luan Borges Teodoro Reis, et al.
Published: (2026)

Can Graphs Improve Tabular Foundation Models?
by: Le, Franck, et al.
Published: (2025)

Active In-Context Learning for Tabular Foundation Models
by: Treerath, Wilailuck, et al.
Published: (2026)

Exploring Fine-Tuning for Tabular Foundation Models
by: Tanna, Aditya, et al.
Published: (2026)

End-to-End Compression for Tabular Foundation Models
by: Zabërgja, Guri, et al.
Published: (2026)

On the Uncertainty Quantification Ability of Tabular Foundation Models
by: Johnson, Tyler R., et al.
Published: (2026)

TabQL: In-Context Q-Learning with Tabular Foundation Models
by: Liu, Qisai, et al.
Published: (2026)

Generalization Can Emerge in Tabular Foundation Models From a Single Table
by: Ma, Junwei, et al.
Published: (2025)

Diffusion Models for Tabular Data Imputation and Synthetic Data Generation
by: Villaizán-Vallelado, Mario, et al.
Published: (2024)