[Colloquium] REMINDER: [TTIC Talks] 1/27 Talks at TTIC: Theodor Misiakiewicz, Stanford University

[Brandie Jones]

*When: *Friday, January 27th* at 11 AM 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=e737c92e-7907-49cc-8f72-af8e0114571d>
)

*Who: *Theodor Misiakiewicz, Stanford University

*Title: *New statistical and computational phenomena from Deep Learning

*Abstract:* Deep learning methodology has presented major challenges for
statistical learning theory. Indeed deep neural networks often operate in
regimes outside the realm of classical statistics and optimization wisdom.
In this talk, we will consider two illustrative examples which clarify some
of these new challenges. The first example considers an instance where
kernel ridge regression with a simple RBF kernel achieves optimal test
error when it perfectly fits the noisy training data. Why can we
interpolate noisy data and still generalize well? Why can overfitting be
benign in kernel ridge regression? The second example---computational in
nature---considers fitting two different smooth ridge functions with deep
neural networks (DNNs). Both can be estimated at the same near-parametric
rate by DNNs trained with unbounded computational resources. However,
empirically, learning becomes much harder for one of these functions when
restricted to DNNs trained using SGD. Why does SGD succeed on some
functions and fail on others? The goal of this talk will be to understand
these two simulations. In particular, we will demonstrate quantitative
theories that can precisely capture both phenomena.

*Bio: *Theodor is a sixth-year PhD candidate in Statistics at Stanford
University working with Prof. Andrea Montanari. Before his Ph.D, he
completed his undergraduate studies in France at Ecole Normale Superieure
de Paris with a focus on pure math and physics. He received a B.Sc. in
Mathematics and a B.Sc. in Physics in 2014, and a M.Sc. in Theoretical
Physics at ICFP in 2016. His interest lies broadly at the intersection of
statistics, machine learning, probability, and computer science.

For more information about him and his work, please visit his personal
webpage. <https://misiakie.github.io/>

*Host: Nati Srebro <nati at ttic.edu>*



--
*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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