Events Calendar

CS Colloquium: Finale Doshi-Velez (Harvard) - Characterizing and Conquering Non-Identifiability in Non-negative Matrix Factorization

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