[CMU AI Seminar] April 25 at 12pm (NSH 3305 & Zoom) -- Xinyi Chen (Princeton) -- A Nonstochastic Control Approach to Optimization -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Dear all,

We look forward to seeing you *this Tuesday (4/25)* from *1**2:00-1:00 PM
(U.S. Eastern time)* for the next talk of this semester's *CMU AI Seminar*,
sponsored by SambaNova Systems <https://sambanova.ai/>. The seminar will be
held in NSH 3305 *with pizza provided *and will be streamed on Zoom.

To learn more about the seminar series or to see the future schedule,
please visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.

Today (4/25), *Xinyi Chen* (Princeton) will be giving a talk titled *"**A
Nonstochastic Control Approach to Optimization**".*

*Title*: A Nonstochastic Control Approach to Optimization

*Talk Abstract*: Selecting the best hyperparameters for a particular
optimization instance, such as the learning rate and momentum, is an
important but nonconvex problem. As a result, iterative optimization
methods such as hypergradient descent lack global optimality guarantees in
general.
  We propose an online nonstochastic control methodology for mathematical
optimization. First, we formalize the setting of meta-optimization, an
online learning formulation of
learning the best optimization algorithm from a class of methods. The
meta-optimization problem over gradient-based methods can be framed as a
feedback control problem over the choice of hyperparameters, including the
learning rate, momentum, and the preconditioner.
  Although the original optimal control problem is nonconvex, we show how
recent methods from online nonstochastic control using convex relaxations
can be used to circumvent the nonconvexity, and obtain regret guarantees
vs. the best offline solution. This guarantees that in meta-optimization,
given a sequence of optimization problems, we can learn a method that
attains convergence comparable to that of the best optimization method in
hindsight from a class of methods.

*Speaker Bio:* Xinyi Chen is a fourth-year Ph.D. student in the Computer
Science department at Princeton University, advised by Prof. Elad Hazan.
Her research is at the intersection of online learning, optimization, and
control. Previously, she obtained her undergraduate degree from Princeton
in Mathematics, where she received the Middleton Miller Prize. She is a
recipient of the NSF Graduate Research Fellowship and a participant of EECS
Rising Stars at UC Berkeley.

*In person: *GHC 6115
*Zoom Link*:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher Trockman
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