Speaker
Lily Zhang
Description
In this talk, we present an overview of anomaly detection from a probabilistic machine learning perspective, with a focus on work emerging from the machine learning literature. First, we discuss empirical failures of deep generative models for anomaly detection and why they occur, as well as their implications for deep generative modeling and anomaly detection. Then, we discuss the endeavor of robust anomaly detection and what is required to achieve it. We conclude with recent work that applies these insights to jet anomaly detection in high-energy physics.
