UTCS AI Colloquia - Fei Sha, University of Southern California, "Probabilistic Models of Learning Latent Similarity"
Signup Schedule: http://apps.cs.utexas.edu/talkschedules/cgi/list_events.cgi
Talk Audience: UTCS Faculty, Grads, Undergrads, Other Interested Parties
Host: Kristen Grauman
Talk Abstract: Inferring similarity among data instances is essential to many learning problems. So far, metric learning is the dominant paradigm. However, similarity is a richer and broader notion than what metrics entail. In this talk, I will describe Similarity Component Analysis (SCA), a new approach overcoming the limitation of metric learning algorithms. SCA is a probabilistic graphical model that discovers latent similarity structures. For a pair of data instances, SCA not only determines whether or not they are similar but also reveal why they are similar (or dissimilar). Empirical studies on the benchmark tasks of multiway classification and link prediction show that SCA outperforms state-of-the-art metric learning algorithms.
Speaker Bio: Fei Sha is the Jack Munushian Early Career Chair and an assistant professor at the University of Southern California, Dept. of Computer Science. His primary research interests are machine learning and its applications to speech and language processing, computer vision, and robotics. He had won outstanding student paper awards at NIPS 2006 and ICML 2004. He was selected as a Sloan Research Fellow in 2013, won an Army Research Office Young Investigator Award in 2012, and was a member of DARPA 2010 Computer Science Study Panel. He has a Ph.D from Computer and Information Science from U. of Pennsylvania and B.Sc and M.Sc in Biomedical Engineering from Southeast University (Nanjing, China).
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