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Distinguished Lecturer Series

Thomas Dietterich

Oregon State University

“Anomaly Detection: Principles, Benchmarking, Explanations, and Theory”

Abstract:

Anomaly detection algorithms are widely 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.

Biography:

Among his research contributions was the application of error-correcting output coding to multiclass classification, the formalization of the multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning, and the development of methods for integrating non-parametric regression trees into probabilistic graphical models (including conditional random fields and latent variable models). Among his writings are Chapter XIV (Learning and Inductive Inference) of the Handbook of Artificial Intelligence, the book Readings in Machine Learning (co-edited with Jude Shavlik), and his frequently-cited review articles Machine Learning Research: Four Current Directions and Ensemble Methods in Machine Learning.

He served as Executive Editor of Machine Learning (1992-98) and helped co-found the Journal of Machine Learning Research. He is currently the editor of the MIT Press series on Adaptive Computation and Machine Learning. He also served as co-editor of the Morgan-Claypool Synthesis Series on Artificial Intelligence and Machine Learning. He is a Fellow of the ACM, AAAI, and AAAS. He served as founding President of the International Machine Learning Society, and he is currently a member of the Steering Committee of the Asian Conference on Machine Learning.

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