  Given an unsupervised outlier detection (OD) algorithm, how can we optimize
its hyperparameter(s) (HP) on a new dataset, without any labels? In this work,
we address this challenging hyperparameter optimization for unsupervised OD
problem, and propose the first systematic approach called HPOD that is based on
meta-learning. HPOD capitalizes on the prior performance of a large collection
of HPs on existing OD benchmark datasets, and transfers this information to
enable HP evaluation on a new dataset without labels. Moreover, HPOD adapts a
prominent sampling paradigm to identify promising HPs efficiently. Extensive
experiments show that HPOD works with both deep (e.g., Robust AutoEncoder) and
shallow (e.g., Local Outlier Factor (LOF) and Isolation Forest (iForest)) OD
algorithms on discrete and continuous HP spaces, and outperforms a wide range
of baselines with on average 58% and 66% performance improvement over the
default HPs of LOF and iForest.
