[Ml-stat-talks] Fwd: ORFE Colloquium: Maryam Fazel, April 4th at 4:30pm, Sherrerd Hall 101

Barbara Engelhardt bee at princeton.edu
Wed Mar 29 12:44:17 EDT 2017

talk of interest.

=== ORFE Colloquium Announcement ===

DATE:  Tuesday, April 4, 2017

TIME:  4:30pm

LOCATION:  Sherrerd Hall, room 101

SPEAKER:  Maryam Fazel, University of Washington

TITLE:  Online Optimization, Smoothing, and Worst-case Competitive Ratio

ABSTRACT:  In Online Optimization, the data in an optimization problem is
revealed over time, and at each step a decision variable needs to be set
without knowing the future data. We consider an online optimization setup
that includes problems such as online resource allocation with a fixed
inventory and the `Adwords' problem popular in online advertising.

We discuss two primal-dual algorithms, with a focus on the competitive
ratio, i.e., the ratio of the objective achieved by the algorithm to that
of the optimal offline sequence of decisions. We give a bound on this ratio
and show how certain smoothing of the objective function can improve the
bound; and for separable functions, how to seek the optimal smoothing by
solving a convex design problem. This approach allows us to design
effective smoothing customized for a given cost function and problem

BIO:  Maryam Fazel is an Associate Professor of Electrical Engineering at
the University of Washington, with adjunct appointments in the departments
of Computer Science and Engineering, Mathematics, and Statistics. Maryam
received her MS and PhD from Stanford University, her BS from Sharif
University of Technology in Iran, and was a postdoctoral scholar at Caltech
prior to joining UW. Her current research interests are in mathematical
optimization and applications in machine learning. She is a recipient of
the NSF Career Award, the UWEE Outstanding Teaching Award, UAI conference
Best Student Paper Award (with her student), and coauthored a paper on
low-rank matrix recovery selected as a Fast-Breaking paper by Science Watch
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