Impact of CausalMixFT Integration with Self-Supervised Pretraining on Tabular Foundation Model Performance

Abstract

Abstract: Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the size of the pretraining dataset are rarely assessed systematically and in a like-for-like manner. This work focuses on foundation models for electrocardiography (ECG) data, one of the most widely captured physiological time series world-wide. We present a comprehensive assessment of pretraining methodologies, covering five different contrastive and non-contrastive self-supervised learning objectives for ECG foundation models, and investigate their s

Research Question

What is the impact of integrating CausalMixFT with self-supervised pretraining (e.g., contrastive learning) on the downstream performance of tabular foundation models, as evaluated on benchmarks like TabBench or TabMNAR?

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