CS Colloquium: Finale Doshi-Velez (Harvard) - Characterizing and Conquering Non-Identifiability in Non-negative Matrix Factorization
Thu, Feb 02, 2017 @ 04:00 PM - 05:00 PM
Conferences, Lectures, & Seminars
Speaker: Finale Doshi-Velez, Harvard
Talk Title: Characterizing and Conquering Non-Identifiability in Non-negative Matrix Factorization
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium. Part of Yahoo! Labs Machine Learning Seminar Series.
Nonnegative matrix factorization (NMF) is a popular dimension reduction technique that produces interpretable decomposition of the data into parts. However, this decomposition is often not identifiable, even beyond simple cases of permutation and scaling. Non-identifiability is an important concern in practical data exploration settings, in which the basis of the NMF factorization may be interpreted as having some kind of meaning: it may be important to know that other non-negative characterizations of the data were also possible. While other studies have provide criteria under which NMF is unique, in this talk I'll discuss when and how an NMF might *not* be unique. Then I'll discuss some novel algorithms for characterizing the posterior in Bayesian NMF.
Biography: Finale Doshi-Velez is an Assistant Professor in Computer Science at Harvard University. Prior to that, she was a NSF CiTraCS postdoctoral fellow at Harvard Medical School and a Marshall Scholar at the University of Cambridge. She completed her PhD at MIT. Her interests lie in the intersection of healthcare and machine learning.
Host: Yan Liu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair