How does the choice of synthetic data generation method (e.g., GANs vs. VAEs) impact the transferability of pr

Abstract

Abstract: 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 Question

How does the choice of synthetic data generation method (e.g., GANs vs. VAEs) impact the transferability of pretrained TFMs to unseen real-world tabular datasets, measured by accuracy degradation on TabMNAR under 20% missing not at random (MNAR) perturbations?

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