CausalMixFT and Fine-Tuning Robustness in Tabular Data Augmentation

Abstract

Abstract: Machine learning (ML) systems have introduced significant advances in various fields, due to the introduction of highly complex models. Despite their success, it has been shown multiple times that machine learning models are prone to imperceptible perturbations that can severely degrade their accuracy. So far, existing studies have primarily focused on models where supervision across all classes were available. In constrast, Zero-shot Learning (ZSL) and Generalized Zero-shot Learning (GZSL) tasks inherently lack supervision across all classes. In this paper, we present a study aimed on evaluat

Research Question

How does CausalMixFT compare to other data augmentation techniques (e.g., SMOTE, MixUp) in terms of fine-tuning robustness on tabular datasets like TabMNAR or Monash, when evaluated using accuracy degradation under adversarial perturbations?

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