Anomaly Detection

Summary

Monitoring large volumes of data and finding anomalous behavior in them is a ubiquitous challenge. The data are of typically high-dimensional, heterogeneous (categorical and numerical), and contain irrelevant attributes and noise. Labels are often scarce and/or expensive, hence unsupervised learning methods are called for.

Our goal is to come up with algorithms that

• Make minimal generative assumptions, and hence apply broadly.
• Give rigorous guarantees, and whose outcomes are easily interpretable.
• Can handle heterogeneous datasets.
• Are highly performant and scale to large volumes and high dimensions.