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Systematic Construction of Anomaly Detection Benchmarks from Real Data., , , , and . CoRR, (2015)End-user feature labeling: a locally-weighted regression approach., , , , , , and . IUI, page 115-124. ACM, (2011)Discovering Anomalies by Incorporating Feedback from an Expert., , , , and . ACM Trans. Knowl. Discov. Data, 14 (4): 49:1-49:32 (2020)Finite Sample Complexity of Rare Pattern Anomaly Detection., , , and . UAI, AUAI Press, (2016)Where Are My Intelligent Assistant's Mistakes? A Systematic Testing Approach., , , , , , , , and . IS-EUD, volume 6654 of Lecture Notes in Computer Science, page 171-186. Springer, (2011)Active Anomaly Detection for time-domain discoveries., , , , , , , , , and . CoRR, (2019)You Are the Only Possible Oracle: Effective Test Selection for End Users of Interactive Machine Learning Systems., , , , , , , , , and 1 other author(s). IEEE Trans. Software Eng., 40 (3): 307-323 (2014)Incorporating Feedback into Tree-based Anomaly Detection., , , , and . CoRR, (2017)Active Anomaly Detection via Ensembles: Insights, Algorithms, and Interpretability., , , and . CoRR, (2019)End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression., , , , , , and . Artif. Intell., (2013)