Advances in Anomaly Detection
ABSTRACT: 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 discoverying rare patterns.
BIO: Dr. Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Distinguished Professor and Director of Intelligent Systems in the School of Electrical Engineering and Computer Science at Oregon State University, where he joined the faculty in 1985. In 1987, he was named a Presidential Young Investigator for the NSF. In 1990, he published, with Dr. Jude Shavlik, the book entitled Readings in Machine Learning, and he also served as the Technical Program Co-Chair of the National Conference on Artificial Intelligence (AAAI-90). From 1992-1998 he held the position of Executive Editor of the journal Machine Learning. The Association for the Advancement of Artificial Intelligence named him a Fellow in 1994, and the Association for Computing Machinery did the same in 2003. In 2000, he co-founded a free electronic journal: The Journal of Machine Learning Research, and he is currently a member of the Editorial Board. Since 2007, he has served as arXiv moderator for Machine Learning. He was Technical Program Chair of the Neural Information Processing Systems (NIPS) conference in 2000 and General Chair in 2001. He is Past-President of the International Machine Learning Society, a member of the IMLS Board, and he also serves on the Advisory Board of the NIPS Foundation. He is President of the Association for the Advancement of Artificial Intelligence.