[Theory] [TTIC Talks] 3/27 TTIC Colloquium: Ohad Shamir, Weizmann Institute

[Brandie Jones]

*When:*         Monday, March 27th* at 11:30pm CT  *


*Where:*       Talk will be given *live, in-person* at

                       TTIC, 6045 S. Kenwood Avenue

                       5th Floor, Room 530


*Virtually:*     via Panopto (Livestream
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=54653fb2-45ec-4212-8d8b-af790078a0cf>
)


*Who:          *Ohad Shamir, Weizmann Institute


*Title:*           Implicit bias in machine learning


*Abstract:  * Most practical algorithms for supervised machine learning
boil down to optimizing the average performance over a training dataset.
However, it is increasingly recognized that although the optimization
objective is the same, the manner in which it is optimized plays a decisive
role in the properties of the resulting predictor. For example, when
training large neural networks, there are generally many weight
combinations that will perfectly fit the training data. However,
gradient-based training methods somehow tend to reach those which, for
example, do not overfit; are brittle to adversarially crafted examples; or
have other interesting properties. In this talk, I'll describe several
recent theoretical and empirical results related to this question.

-- 
*Brandie Jones *
*Executive **Administrative Assistant*
Toyota Technological Institute
6045 S. Kenwood Avenue
Chicago, IL  60637
www.ttic.edu
Working Remotely on Tuesdays
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