  Automated machine learning has been widely researched and adopted in the
field of supervised classification and regression, but progress in unsupervised
settings has been limited. We propose a novel approach to automate outlier
detection based on meta-learning from previous datasets with outliers. Our
premise is that the selection of the optimal outlier detection technique
depends on the inherent properties of the data distribution. We leverage
optimal transport in particular, to find the dataset with the most similar
underlying distribution, and then apply the outlier detection techniques that
proved to work best for that data distribution. We evaluate the robustness of
our approach and find that it outperforms the state of the art methods in
unsupervised outlier detection. This approach can also be easily generalized to
automate other unsupervised settings.
