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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:20241107T114355Z
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