Corpus ID: 220525904

Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies

@inproceedings{Chitra2021QuantifyingAR,
  title={Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies},
  author={Uthsav Chitra and Kimberly Ding and Benjamin J. Raphael},
  booktitle={ICML},
  year={2021}
}
Anomaly estimation, or the problem of finding a subset of a dataset that differs from the rest of the dataset, is a classic problem in machine learning and data mining. In both theoretical work and in applications, the anomaly is assumed to have a specific structure defined by membership in an $\textit{anomaly family}$. For example, in temporal data the anomaly family may be time intervals, while in network data the anomaly family may be connected subgraphs. The most prominent approach for… Expand

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