[Sds-seminars] [Sds-announce] Wednesday: Omar Montasser on "What, When, and How can we Learn Adversarially Robustly?"

[Dan Spielman]

OMAR MONTASSER, Toyota Technological Institute at Chicago
Title: What, When, and How can we Learn Adversarially Robustly?
Wednesday, February 08, 20234:00PM to 5:00PM
Mason Lab 211 see map
<http://maps.google.com/?q=9+Hillhouse+Ave%2C+New+Haven%2C+CT%2C+06511%2C+us>

9 Hillhouse Ave
New Haven, CT 06511
Website <https://home.ttic.edu/~omar/>
Information and Abstract:

Despite extraordinary progress, current machine learning systems have been
shown to be brittle against adversarial examples: seemingly innocuous but
carefully crafted perturbations of test examples that cause machine
learning predictors to misclassify. Can we learn predictors robust to
adversarial examples? and how? There has been much empirical interest in
this major challenge in machine learning, and in this talk, we will present
a theoretical perspective. We will illustrate the need to go beyond
traditional approaches and principles, such as empirical (robust) risk
minimization, and present new algorithmic ideas with stronger robust
learning guarantees.

Bio:

Omar Montasser is a PhD candidate at TTI-Chicago advised by Nathan Srebro.
His research broadly explores the theory and foundations of machine
learning. Recently, his research has focused on understanding and
characterizing adversarially robust learning, and on designing learning
algorithms with provable robustness guarantees under different settings.
His work has been recognized by a best student paper award at COLT (2019).

*In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with
the option of virtual participation (*
https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa
)

*3:30pm <https://0.0.0.10/> -   Pre-talk meet and greet teatime - Dana
House, 24 Hillhouse Avenue *
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.yale.edu/pipermail/sds-seminars/attachments/20230207/0c82c07a/attachment.html>
-------------- next part --------------
-- 
Sds-announce mailing list
Sds-announce at mailman.yale.edu
https://mailman.yale.edu/mailman/listinfo/sds-announce