[Colloquium] REMINDER: Talks at TTIC: Sivan Sabato, Microsoft Research New England

[Dawn Ellis]

When:     Thursday, January 30th at 11am

Where:    TTIC, 6045 S Kenwood Avenue, 5th Floor, Room #526

Speaker:  Sivan Sabato, Microsoft Research New England

Title:       Learning with Lower Information Costs

Abstract:

In this talk I will consider learning with lower information costs,
focusing on linear regression. Linear regression is one of the most
widely used methods for prediction and forecasting, with widespread
uses in many fields such as natural sciences, economy and medicine.
I will show how to improve the information costs of linear regression in
two settings. First, I will present a new estimation algorithm for the
standard supervised regression setting. This is the first efficient
estimator that enjoys minimax optimal sample complexity, up to log
factors, for general heavy tailed distributions. The technique is
general and can be applied to a larger class of smooth and strongly
convex losses. Second, I will consider the challenge of using crowd
sourcing for labeling in tasks that usually require experts, and show
how to achieve this using linear regression combined with a feature
multi-selection approach.

Based on Joint work with Daniel Hsu and Adam Kalai.


Bio:
Sivan Sabato is a post-doctoral researcher at Microsoft Research New
England.
Her main research interests are in statistical machine learning theory and
its applications. Sivan received her M.Sc. in Computer Science from the
Technion,
and her Ph.D. in Computer Science from the Hebrew University of Jerusalem.
She is an alumna of the Adams fellowship program for outstanding Ph.D.
students, and has been awarded several honors, including the Wolf Prize for
outstanding M.Sc. thesis, the Google Anita Borg Scholarship, and the Intel
Excellence Award.

Host:  Greg Shakhnarovich, gregory at ttic.edu

-- 
*Dawn Ellis*
Administrative Coordinator,
Bookkeeper
773-834-1757
dellis at ttic.edu

TTIC
6045 S. Kenwood Ave.
Chicago, IL. 60637
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