Missingness-Aware Dynamic Ensemble Weighting (M-DEW) for Improved Prediction with Missing Data

Missingness-Aware Dynamic Ensemble Weighting (M-DEW) is a novel AutoML technique that constructs a set of two-stage imputation-prediction pipelines, trains each component separately, and dynamically calculates a set of pipeline weights for each sample during inference time to improve performance and calibration on downstream machine learning tasks over standard model averaging techniques.