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Synthetic Tabular Data Validation: A Divergence-Based Approach
May 14, 2024, 4:42 a.m. | Patricia A. Apell\'aniz, Ana Jim\'enez, Borja Arroyo Galende, Juan Parras, Santiago Zazo
cs.LG updates on arXiv.org arxiv.org
Abstract: The ever-increasing use of generative models in various fields where tabular data is used highlights the need for robust and standardized validation metrics to assess the similarity between real and synthetic data. Current methods lack a unified framework and rely on diverse and often inconclusive statistical measures. Divergences, which quantify discrepancies between data distributions, offer a promising avenue for validation. However, traditional approaches calculate divergences independently for each feature due to the complexity of joint …
abstract arxiv cs.ai cs.lg current data data validation divergence diverse ever fields framework generative generative models highlights metrics robust statistical synthetic synthetic data tabular tabular data type validation
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