Machine Learning Seminar

Anomaly mining is critical for a variety of real-world tasks in security, finance, medicine, and so on. Despite its immense popularity however, the problem is under-specified for many practical applications, such as insider threat detection, as the true goals are often difficult to specify. Research community has long focused on a few simple formulations that do not meet the needs of modern anomaly mining tasks in complex systems. The problem of anomaly mining presents pressing challenges along three main dimensions: in providing precise ‘D’efinitions of what an anomaly is, in effectively ‘D’etecting anomalies, and finally in providing practitioners with actionable ‘D’escriptions of the detected anomalies. My research focuses broadly on building new descriptive models and methods for anomaly mining in the real world, and addresses challenges arising from scale, data multimodality, dynamics, robustness…


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