Zaid Ahmad

One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Data

A single pre-training pipeline that builds transformer-based imputation specialists through three components: an entry-wise featurization that recasts imputation as supervised prediction over row--column context, a synthetic data generator with pluggable missingness modules, and prior-data fitting on millions of synthetic tables.