Robustness Comparison of Tabular Foundation Models and Traditional ML on Missing-Value Datasets

The development of tabular foundation models (TFMs) has accelerated in recent years, showing strong potential to outperform traditional ML methods for structured data. A key finding is that TFMs can be pretrained entirely on synthetic datasets, opening opportunities to design data generators that encourage desirable model properties. Prior work has mainly focused on crafting high-quality priors over generators to improve overall pretraining performance. Our insight is that parameterizing the generator distribution enables an adversarial robustness perspective: during training, we can adapt the

Research goal: How do tabular foundation models pretrained on synthetic data with adversarial noise compare in robustness to traditional ML models on tabular datasets with missing values, as measured by accuracy on the TabMNAR benchmark?

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