  Automated machine learning (AutoML) can produce complex model ensembles by
stacking, bagging, and boosting many individual models like trees, deep
networks, and nearest neighbor estimators. While highly accurate, the resulting
predictors are large, slow, and opaque as compared to their constituents. To
improve the deployment of AutoML on tabular data, we propose FAST-DAD to
distill arbitrarily complex ensemble predictors into individual models like
boosted trees, random forests, and deep networks. At the heart of our approach
is a data augmentation strategy based on Gibbs sampling from a self-attention
pseudolikelihood estimator. Across 30 datasets spanning regression and
binary/multiclass classification tasks, FAST-DAD distillation produces
significantly better individual models than one obtains through standard
training on the original data. Our individual distilled models are over 10x
faster and more accurate than ensemble predictors produced by AutoML tools like
H2O/AutoSklearn.
