:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Nie, Junfeng, Jin, Alvin, Chen, Xiaohui
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2605.28198
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Comparing Bottom-Up and Top-Down Steering Approaches on In-Context Learning Tasks
by: Brumley, Madeline, et al.
Published: (2024)

ReeFRAME: Reeb Graph based Trajectory Analysis Framework to Capture Top-Down and Bottom-Up Patterns of Life
by: Gudavalli, Chandrakanth, et al.
Published: (2024)

Hierarchical Conditional Tabular GAN for Multi-Tabular Synthetic Data Generation
by: Ågren, Wilhelm, et al.
Published: (2024)

Privacy-Preserving Tabular Synthetic Data Generation Using TabularARGN
by: Sidorenko, Andrey, et al.
Published: (2025)

TAEGAN: Generating Synthetic Tabular Data For Data Augmentation
by: Li, Jiayu, et al.
Published: (2024)

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

FEST: A Unified Framework for Evaluating Synthetic Tabular Data
by: Niu, Weijie, et al.
Published: (2025)

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

A Hybrid Machine Learning Approach for Synthetic Data Generation with Post Hoc Calibration for Clinical Tabular Datasets
by: Mahin, Md Ibrahim Shikder, et al.
Published: (2025)

Synthetic Tabular Data Generation: A Comparative Survey for Modern Techniques
by: Challagundla, Raju, et al.
Published: (2025)

CARTGen-IR: Synthetic Tabular Data Generation for Imbalanced Regression
by: Pinheiro, António Pedro, et al.
Published: (2025)

Structured Evaluation of Synthetic Tabular Data
by: Yang, Scott Cheng-Hsin, et al.
Published: (2024)

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data
by: Tiwald, Paul, et al.
Published: (2025)

Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework
by: Sidorenko, Andrey, et al.
Published: (2025)

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes
by: Jiang, Mingxuan, et al.
Published: (2025)

CuTS: Customizable Tabular Synthetic Data Generation
by: Vero, Mark, et al.
Published: (2023)

Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generation
by: Long, Yunbo, et al.
Published: (2025)

Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets
by: Marin, Javier
Published: (2022)

MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection
by: Paim, Kayua Oleques, et al.
Published: (2025)

Tab-Shapley: Identifying Top-k Tabular Data Quality Insights
by: Padala, Manisha, et al.
Published: (2025)

When Does Bottom-up Beat Top-down in Hierarchical Community Detection?
by: Dreveton, Maximilien, et al.
Published: (2023)

How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data
by: Stoian, Mihaela Cătălina, et al.
Published: (2024)

SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data
by: Lautrup, Anton Danholt, et al.
Published: (2024)

Synthetic Tabular Data Generation for Imbalanced Classification: The Surprising Effectiveness of an Overlap Class
by: D'souza, Annie, et al.
Published: (2024)

Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study
by: Panagiotou, Emmanouil, et al.
Published: (2024)

FASTGEN: Fast and Cost-Effective Synthetic Tabular Data Generation with LLMs
by: Nguyen, Anh, et al.
Published: (2025)

Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?
by: Swanberg, Marika, et al.
Published: (2025)

Synthetic Tabular Data: Methods, Attacks and Defenses
by: Cormode, Graham, et al.
Published: (2025)

Privacy-Preserving Fair Synthetic Tabular Data
by: Sarmin, Fatima J., et al.
Published: (2025)

Robust Spectral Watermark for Synthetic Tabular Data
by: Zhao, Yizhou, et al.
Published: (2025)

Generating Synthetic Relational Tabular Data via Structural Causal Models
by: Hoppe, Frederik, et al.
Published: (2025)

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

Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets
by: Nagesh, Nitish, et al.
Published: (2026)

TabSCM: A practical Framework for Generating Realistic Tabular Data
by: Jacob, Sven, et al.
Published: (2026)

Synthetic Tabular Data Detection In the Wild
by: Kindji, G. Charbel N., et al.
Published: (2025)

Cross-table Synthetic Tabular Data Detection
by: Kindji, G. Charbel N., et al.
Published: (2024)

Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios
by: Apellániz, Patricia A., et al.
Published: (2024)

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models
by: Kapar, Jan, et al.
Published: (2025)

A Systematic Framework for Tabular Data Disentanglement
by: Tjuawinata, Ivan, et al.
Published: (2026)

TabTreeFormer: Tabular Data Generation Using Hybrid Tree-Transformer
by: Li, Jiayu, et al.
Published: (2025)