Cover Image

What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kapar, Jan, Koenen, Niklas, Jullum, Martin
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Conditional Feature Importance with Generative Modeling Using Adversarial Random Forests
by: Blesch, Kristin, et al.
Published: (2025)

Machine Learning in Epidemiology
by: Wright, Marvin N., et al.
Published: (2026)

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

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

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

Toward Understanding the Disagreement Problem in Neural Network Feature Attribution
by: Koenen, Niklas, et al.
Published: (2024)

Interpreting Deep Neural Networks with the Package innsight
by: Koenen, Niklas, et al.
Published: (2023)

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

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)

Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
by: Pereira, Tomás, et al.
Published: (2026)

How Well Does Your Tabular Generator Learn the Structure of Tabular Data?
by: Jiang, Xiangjian, et al.
Published: (2025)

SHAP Distance: An Explainability-Aware Metric for Evaluating the Semantic Fidelity of Synthetic Tabular Data
by: Yu, Ke, et al.
Published: (2025)

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

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

Gradient-based Explanations for Deep Learning Survival Models
by: Langbein, Sophie Hanna, et al.
Published: (2025)

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

Improving the Weighting Strategy in KernelSHAP
by: Olsen, Lars Henry Berge, 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)

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

CARTGen-IR: Synthetic Tabular Data Generation for Imbalanced Regression
by: Pinheiro, António Pedro, 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)

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

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

Evaluating Generative Models for Tabular Data: Novel Metrics and Benchmarking
by: Herurkar, Dayananda, et al.
Published: (2025)

A Systematic Evaluation of Generative Models on Tabular Transportation Data
by: Wang, Chengen, et al.
Published: (2025)

MCCE: Monte Carlo sampling of realistic counterfactual explanations
by: Redelmeier, Annabelle, et al.
Published: (2021)

Generating Reliable Synthetic Clinical Trial Data: The Role of Hyperparameter Optimization and Domain Constraints
by: Hahn, Waldemar, et al.
Published: (2025)

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

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

TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data
by: Chundawat, Vikram S, et al.
Published: (2022)

Your Assumed DAG is Wrong and Here's How To Deal With It
by: Padh, Kirtan, et al.
Published: (2025)

shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python
by: Jullum, Martin, et al.
Published: (2025)

FlagGAM: Rule-Based Generalized Additive Modeling for Explainable Tabular Prediction
by: Zhao, Zijie, et al.
Published: (2026)

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 Flight Data Generation Using Generative Models
by: Aly, Karim, et al.
Published: (2026)

CausalWrap: Model-Agnostic Causal Constraint Wrappers for Tabular Synthetic Data
by: Asiaee, Amir, et al.
Published: (2026)

Protect and Extend -- Using GANs for Synthetic Data Generation of Time-Series Medical Records
by: Ashrafi, Navid, et al.
Published: (2024)