Impact of Adversarial Pre-training on TabPFN Robustness to Distribution Shifts in Tabular Datasets

Abstract

Abstract: The challenges of handling imbalanced datasets in machine learning significantly affect the model performance and predictive accuracy. Classifiers tend to favor the majority class, leading to biased training and poor generalization of minority classes. Initially, the model incorrectly treats the target variable as an independent feature during data generation, resulting in suboptimal outcomes. To address this limitation, the model was adjusted to more effectively manage target variable generation and mitigate the issue. This study employed advanced techniques for synthetic data generation, suc

Research Question

What is the impact of incorporating adversarial training during pre-training of TabPFN on its robustness to distribution shifts in tabular datasets, as measured by accuracy degradation on out-of-distribution benchmarks like TabShift?

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