Out-of-Distribution (OOD) detection in machine learning refers to the problem of detecting whether the machine learning model's output can be trusted at inference time. This problem has been described qualitatively in the literature, and a number of ad hoc tests for OOD detection have been proposed. In this talk we outline a principled approach to the OOD detection problem, by first defining the problem through a hypothesis test that includes both the input distribution and the learning algorithm. Our definition provides insights for the construction of good tests for OOD detection. We then propose a multiple testing inspired procedure to systematically combine any number of different OOD test statistics using conformal p-values. Our approach allows us to provide strong guarantees on the probability of incorrectly classifying an in-distribution sample as OOD. In our experiments, we find that the tests proposed in prior work perform well in specific settings, but not uniformly well across different types of OOD instances. In contrast, our proposed method that combines multiple test statistics performs uniformly well across different datasets, neural networks and OOD instances. We will end the talk with a discussion of the application of the multiple testing approach to the problem of hallucination detection in LLMs.
Prof. Veeravalli received the Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign in 1992, the M.S. degree from Carnegie-Mellon University in 1987, and the B.Tech degree from Indian Institute of Technology, Bombay (Silver Medal Honors) in 1985. He is currently the Henry Magnuski Professor in the Department of Electrical and Computer Engineering (ECE) at the University of Illinois at Urbana-Champaign, where he also holds appointments with the Coordinated Science Laboratory (CSL), the Department of Statistics, and the Discovery Partners Institute. He was on the faculty of the School of ECE at Cornell University before he joined Illinois in 2000. He served as a program director for communications research at the U.S. National Science Foundation during 2003-2005. His research interests span the theoretical areas of statistical inference, machine learning, and information theory, with applications to AI, data science, wireless communications, and sensor networks. He is currently the Editor-in-Chief of the IEEE Transactions on Information Theory. He is a Fellow of the IEEE and a Fellow of the Institute of Mathematical Statistics (IMS). Among the awards he has received for research and teaching are the IEEE Browder J. Thompson Best Paper Award, the U.S. Presidential Early Career Award for Scientists and Engineers (PECASE), the Abraham Wald Prize in Sequential Analysis (twice), the Fulbright-Nokia Chair in Information and Communication Technologies, and the Distinguished Alumnus Award (DAA) from IIT Bombay.