Comparative Analysis of CausalMixFT and Diffusion Models for High-Dimensional Tabular Data on the TabPF Benchmark

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 CausalMixFT and diffusion models compare in terms of inference latency and sample fidelity when scaled to tabular datasets with 1000+ dimensions on the TabPF benchmark?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.1/10.