Anomaly detection algorithms are widely applied in data cleaning, fraud detection, and cybersecurity. This talk will begin by defining various anomaly detection tasks and then focus on unsupervised anomaly detection. It will present a benchmarking study comparing eight state-of-the-art methods. Then it will discuss methods for explaining anomalies to experts and incorporating expert feedback into the anomaly detection process. The talk will conclude with a theoretical (PAC-learning) framework for formalizing a large family of anomaly detection algorithms based on discovering rare patterns.
Dr. Thomas G. Dietterich is Distinguished Professor (Emeritus) in Computer Science at Oregon State University and Chief Scientist of BigML, a machine learning startup company. One of the founders of the field of machine learning, Dietterich has published more than 130 scientific papers. He serves as Past President of the Association for the Advancement of Artificial Intelligence, founding President of the International Machine Learning Society, and former Executive Editor of the journal Machine Learning. Dietterich's research seeks methods for enabling AI systems to robustly deal with "unknown unknowns". He also leads projects in applying AI to biological conservation, management of invasive species, and policies for controlling wildfire. He is applying machine learning methods to automatically detect errors in big data applications including weather data collected by the Trans-Africa Hydrometeorological Observatory (TAHMO), which is a sustainable development project throughout sub-Saharan Africa. Dietterich is a Fellow of the Association for the Advancement of Science, the Association for Computing Machinery, and the Association for the Advancement of Artificial Intelligence.