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PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
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SUMMARY:Brown bag: Madeleine Udell
DESCRIPTION:Title: Learning with Big Messy Data\nSpeaker: Madeleine
	 Udell\nAbstract: Modern data sets are often big and messy: they may
	 feature a mixture of real\, boolean\, ordinal\, and nominal values; and
	 often many (or even most) of the values of interest are missing.\nMy
	 research centers on exploiting structure in big messy data sets to infer
	 missing data\, detect patterns\, speed up optimization\, and promote
	 better decisions.\n\nAs a case study\, this talk will introduce
	 Generalized Low Rank Models (GLRMs)\, a class of optimization problems
	 designed to uncover structure in big messy data sets.\nThese models
	 generalize many well known techniques in data analysis\, such as
	 (standard or robust) PCA\, nonnegative matrix factorization\, matrix
	 completion\, and k-means.\nWe'll discuss use GLRMs to impute missing
	 values; to design recommender systems; to and to perform dimensionality
	 reduction; all in a setting with heterogeneous and missing data.\n\nThe
	 resulting optimization problems often have millions or even billions of
	 parameters.\nWe'll discuss efficient optimization techniques for these
	 problems\, and will conclude with a discussion of outstanding challenges
	 and open problems in this area.
LOCATION:Gates 122
UID:2017-02-07
STATUS:CONFIRMED
DTSTART:20170207T170000Z
DTEND:20170207T180000Z
LAST-MODIFIED:20170214T013622Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T131742Z
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