Ensembles for unsupervised outlier detection: challenges and research questions a position paper

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Progress in Outlier Detection Techniques: A Survey (2019, methodology): “Some techniques [181], [184] are static and do not involve any detector selection methods.”
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection (2015, methodology): “Let us note that some methods have been proposed to combine single outlier detectors into an ensemble [Lazarevic and Kumar 2005; Gao and Tan 2006; Nguyen et al. 2010; Kriegel et al. 2011a; Schubert et al. 2012; Liu et al. 2012; Zimek et al. 2013a, 2013, 2014].”
A comparative evaluation of outlier detection algorithms: Experiments and analyses (2018, background): “Other promising directions leading the research on outlier detection also focus on ensemble learning [32] and detecting outliers from multi-view data [11].”
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy (2019, background): “Previous work mostly achieves outlier detection using unsupervised learning methods ([25, 26, 27]), while novelty detection typically assumes a normal dataset is available for training, and is realized by semi-supervised learning ([28, 29]).”