[Ml-stat-talks] Princeton Optimization Seminar: Elad Hazan, Wed. March 11, 4:30 PM

Amir Ali Ahmadi a_a_a at princeton.edu
Thu Mar 5 08:35:35 EST 2015


-----   Princeton Optimization Seminar   -----


DATE:  Wednesday, March 11 (note the unusual date)

TIME:  4:30 pm

LOCATION:  Sherrerd Hall 101

SPEAKER:  Elad Hazan, Princeton University

TITLE:  Projection-Free Optimization and Online Learning

ABSTRACT:
Modern large data sets prohibit any super-linear time operations. This motivates the study of iterative optimization algorithms with low complexity per iteration. The computational bottleneck in applying state-of-the-art iterative methods is many times the so-called "projection step". We consider projection-free optimization/learning that replaces projections by more efficient linear optimization steps. We describe the first linearly-converging algorithm of this type for polyhedral sets and the implications for attaining optimal-rate stochastic optimization and online learning algorithms.
Bio:
Elad Hazan joined the faculty at Princeton in 2015 from the Technion, where he had been an associate professor of operations research. His research focuses on the design and analysis of algorithms for basic problems in machine learning and optimization. He is the recipient of (twice) the IBM Goldberg best paper award in 2012 for contributions to sublinear time algorithms for machine learning, and in 2008 for decision making under uncertainty,  a European Research Council grant , a Marie Curie fellowship and a Google Research Award (twice). He serves on the steering committee of the Association for Computational Learning and is a program co-chair for COLT 2015.
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