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@INPROCEEDINGS{valko2011conditional,
  author = {Michal Valko and Hamed Valizadegan and Branislav Kveton Gregory F.
	Cooper and Milos Hauskrecht},
  title = {Conditional Anomaly Detection Using Soft Harmonic Functions: An Application
	to Clinical Alerting},
  booktitle = {The 28th International Conference on Machine Learning Workshop on
	Machine Learning for Global Challenges},
  year = {2011},
  month = {June},
  abstract = {Timely detection of concerning events is an important problem in clinical
	practice. In this paper, we consider the problem of conditional anomaly
	detection that aims to identify data instances with an unusual response,
	 such as the omission of an important lab test. 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 in detecting unusual labels on a real-world
	electronic health record dataset and compare it to several baseline
	approaches.},
  owner = {misovalko},
  timestamp = {2011.06.21}
}