Abstract: Interpreting how neural networks work is a crucial and challenging task in machine learning. In this talk, I will discuss a novel framework, namely neural interaction detector (NID), for interpreting complex neural networks by detecting statistical interactions captured by the neural networks. Furthermore, we can construct a more interpretable generalized additive model that achieve similar prediction performance as the original neural networks. Experiment results on several applications, such as recommender systems, image recognition, sentiment prediction, demonstrate the effectiveness of NID.

Bio: Yan Liu is a Professor in the Computer Science Department and the Director of Machine Learning Center at University of Southern California. She was a Research Staff Member at IBM Research in 2006-2010 and Chief Scientist in Didi Chuxing in 2018. She received her Ph.D. degree from Carnegie Mellon University. Her research interest is machine learning and its applications to climate science, health care and sustainability. She has received several awards, including NSF CAREER Award, Okawa Foundation Research Award, New Voices of Academies of Science, Engineering, and Medicine, Biocom Catalyst Award Winner, ACM Dissertation Award Honorable Mention, Best Paper Award in SIAM Data Mining Conference.