[Theory] [Talks at TTIC] 9/15 TTIC Colloquium: Nadav Cohen, Tel Aviv University

[Brandie Jones via Theory]

*When:*        Monday, September 15th at *11:30am CT*

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

                       TTIC, 6045 S. Kenwood Avenue

                       5th Floor, Room 529

**In-Person Only**


*Who: *         Nadav Cohen, Tel Aviv University

*Title:*          Implicit Biases of Gradient Descent in Offline System
Identification and Optimal Control
*Abstract:  *When learning to control a critical system (e.g., in
healthcare or manufacturing), trial and error is often prohibitively
dangerous and/or costly.  A natural alternative approach is offline system
identification and optimal control: using pre-recorded data for offline
learning of a system model, and then using the system model for offline
learning of an optimal controller.  When implemented with overparameterized
models (e.g., neural networks) trained via gradient descent (GD), this
approach achieves remarkable success.  For example, it enables reducing CO2
emissions of industrial manufacturing plants by up to 20%.  This success is
driven by implicit biases of GD, which yield not only in-distribution
generalization, but also out-of-distribution generalization.  Towards
elucidating this phenomenon, I will present a series of works that
theoretically analyze implicit biases of GD when applied to
overparameterized linear models in offline system identification and
optimal control.  The results I will present offer theoretical explanations
for the success of GD in controlling critical systems, and suggest
potential avenues for enhancing this success.

*Bio: *Nadav Cohen is an Associate Professor of Computer Science & AI at
Tel Aviv University, studying the theoretical and algorithmic foundations
of neural networks. He is also the CTO, President and a Co-Founder at
Imubit, a company that applies neural networks for control of industrial
manufacturing plants, thereby reducing CO2 emissions while improving
yield.  Nadav earned a BSc in electrical engineering and a BSc in
mathematics (both summa cum laude) at the Technion. He obtained his PhD
(direct track, summa cum laude) at the School of Computer Science and
Engineering in the Hebrew University. Subsequently, he was a postdoctoral
research scholar at the Institute for Advanced Study in Princeton. For his
contributions to the foundations of neural networks, Nadav received several
honors and awards, including the ERC Starting Grant, the Google Research
Scholar Award, the Google Doctoral Fellowship in Machine Learning, the
Rothschild Postdoctoral Fellowship, and the Zuckerman Postdoctoral
Fellowship.


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



*Brandie Jones *
*Executive **Administrative Assistant*
*Outreach Administrator *
Toyota Technological Institute
6045 S. Kenwood Avenue
Chicago, IL  60637
www.ttic.edu
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