Cover Image

TabSynDex: A Universal Metric for Robust Evaluation of Synthetic Tabular Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chundawat, Vikram S, Tarun, Ayush K, Mandal, Murari, Lahoti, Mukund, Narang, Pratik
Format:	Preprint
Published:	2022
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Unified Framework for Continual Learning and Unlearning
by: Chatterjee, Romit, et al.
Published: (2024)

EcoVal: An Efficient Data Valuation Framework for Machine Learning
by: Tarun, Ayush K, et al.
Published: (2024)

Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models
by: Sharma, Aakash Sen, et al.
Published: (2024)

ConDa: Fast Federated Unlearning with Contribution Dampening
by: Chundawat, Vikram S, et al.
Published: (2024)

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

Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents
by: Talokar, Nivya, et al.
Published: (2026)

SynQP: A Framework and Metrics for Evaluating the Quality and Privacy Risk of Synthetic Data
by: Hu, Bing, et al.
Published: (2026)

TabImpute: Universal Zero-Shot Imputation for Tabular Data
by: Feitelberg, Jacob, et al.
Published: (2025)

TabPFGen -- Tabular Data Generation with TabPFN
by: Ma, Junwei, et al.
Published: (2024)

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

SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table Extraction
by: Bradley, Ethan, et al.
Published: (2024)

Robust Spectral Watermark for Synthetic Tabular Data
by: Zhao, Yizhou, 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)

TabDDPM: Modelling Tabular Data with Diffusion Models
by: Kotelnikov, Akim, et al.
Published: (2022)

TabStruct: Measuring Structural Fidelity of Tabular Data
by: Jiang, Xiangjian, et al.
Published: (2025)

SDQM: Synthetic Data Quality Metric for Object Detection Dataset Evaluation
by: Zenith, Ayush, et al.
Published: (2025)

FedTabDiff: Federated Learning of Diffusion Probabilistic Models for Synthetic Mixed-Type Tabular Data Generation
by: Sattarov, Timur, et al.
Published: (2024)

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

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

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

PATE-TabTransGAN: Differentially Private Synthetic Tabular Data Generation via Transformer-Based Student Discrimination
by: Youssef, M., et al.
Published: (2026)

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

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

TabChange: Precise Attribute Changes in Tabular Data
by: Dahal, Arjun, et al.
Published: (2026)

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

LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion
by: Long, Yunbo, et al.
Published: (2025)

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

DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
by: Mandi, Zhao, et al.
Published: (2025)

Robust Detection of Synthetic Tabular Data under Schema Variability
by: Kindji, G. Charbel N., et al.
Published: (2025)

Uncertainty-Aware Tabular Prediction: Evaluating VBLL-Enhanced TabPFN in Safety-Critical Medical Data
by: Ramalingam, Madhushan
Published: (2025)

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

MambaTab: A Plug-and-Play Model for Learning Tabular Data
by: Ahamed, Md Atik, et al.
Published: (2024)

TabNSA: Native Sparse Attention for Efficient Tabular Data Learning
by: Eslamian, Ali, et al.
Published: (2025)

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

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

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

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

Distill to Delete: Unlearning in Graph Networks with Knowledge Distillation
by: Sinha, Yash, et al.
Published: (2023)

TabText: Language-Based Representations of Tabular Health Data for Predictive Modelling
by: Carballo, Kimberly Villalobos, et al.
Published: (2022)

TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
by: Alishahi, Meysam, et al.
Published: (2026)