Novel Metrics for Tabular Generative Models Under Adversarial Perturbations in Mixed Data Types

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: Do the novel metrics for tabular generative models demonstrate robustness against adversarial perturbations in mixed data types, and how does this compare to traditional evaluation approaches?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.