Adaptive Binning Improves Self-Supervised Learning on Medical Tabular Data

Adaptive Binning introduces a feature-wise coarse-to-fine curriculum for discretization in tabular self-supervised learning, replacing fixed global quantile binning. The method targets clinical tabular data where labels are scarce and structured variables are abundant.