  There are currently many barriers that prevent non-experts from exploiting
machine learning solutions ranging from the lack of intuition on statistical
learning techniques to the trickiness of hyperparameter tuning. Such barriers
have led to an explosion of interest in automated machine learning (AutoML),
whereby an off-the-shelf system can take care of many of the steps for
end-users without the need for expertise in machine learning. This paper
presents Ensemble Squared (Ensemble$^2$), an AutoML system that ensembles the
results of state-of-the-art open-source AutoML systems. Ensemble$^2$ exploits
the diversity of existing AutoML systems by leveraging the differences in their
model search space and heuristics. Empirically, we show that diversity of each
AutoML system is sufficient to justify ensembling at the AutoML system level.
In demonstrating this, we also establish new state-of-the-art AutoML results on
the OpenML tabular classification benchmark.
