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@INPROCEEDINGS{valko2011conditionala,
  author = {Michal Valko and Branislav Kveton and Hamed Valizadegan  and Gregory
	F. Cooper and Milos Hauskrecht},
  title = {Conditional Anomaly Detection with Soft Harmonic Functions},
  booktitle = {Proceedings of the 2011 IEEE International Conference on Data Mining},
  year = {2011},
  month = {June},
  abstract = {In this paper, we consider the problem of conditional anomaly detection
	that aims to identify data instances with an unusual response or
	a class label.  We develop a new non-parametric approach for conditional
	anomaly detection based on the soft harmonic solution, with which
	we estimate the confidence of the label to detect anomalous mislabeling.
	We further regularize the solution to avoid the detection of  isolated
	examples and  examples on the boundary of the distribution support.
	We demonstrate the efficacy of the proposed method on several synthetic
	and UCI ML datasets in detecting unusual labels when compared to
	several baseline approaches. We also evaluate the performance of
	our method on a real-world electronic health record dataset where
	we seek to identify unusual patient-management decisions.},
  owner = {misovalko},
  timestamp = {2011.06.21}
}