Novel Structural Fidelity Metrics for Generative Tabular Models Versus Traditional MMD and WF on High-Dimensional Sparse Data

Generative models have revolutionized multiple domains, yet their application to tabular data remains underexplored. Evaluating generative models for tabular data presents unique challenges due to structural complexity, large-scale variability, and mixed data types, making it difficult to intuitively capture intricate patterns. Existing evaluation metrics offer only partial insights, lacking a comprehensive measure of generative performance. To address this limitation, we propose three novel evaluation metrics: FAED, FPCAD, and RFIS. Our extensive experimental analysis, conducted on three stan

Research goal: How do novel structural fidelity metrics for generative tabular models compare to traditional metrics like MMD or WF in capturing discriminative performance on high-dimensional sparse datasets when paired with downstream classifiers?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10.