Time+Place: Wednesday 18/05/2016 14:30 Room 337-8 Taub Bld.
Title: Some limitations and possibilities toward data-driven optimization
Speaker: Yaron Singer - COLLOQUIUM LECTURE - Note unusual day http://people.seas.harvard.edu/~yaron/
Affiliation: Harvard University.
Host: Seffi Naor

Abstract:


As we grow highly dependent on data for making predictions, we translate 
these predictions into models that help us make informed decisions.  But 
how do the guarantees we have on predictions translate to guarantees on 
decisions? In many cases, we learn models from sampled data and then aim 
to use these models to make decisions.  This intuitive approach turns out
to have non-trivial limitations.  In some cases, despite having access
to large data sets, the current frameworks we have for learnability do not 
suffice to guarantee desirable outcomes.  In other cases, the learning 
techniques we have introduce estimation errors which can result in poor 
outcomes and stark impossibility results.  In this talk we will formalize 
some of these ideas using convex and combinatorial optimization and discuss 
some possibility and impossibility results of this agenda.

Short Bio:

Yaron Singer is an Assistant Professor of Computer Science at Harvard University.  
He was previously a postdoctoral researcher at Google Research and obtained his PhD 
from UC Berkeley.  He is the recipient of the NSF CAREER award, 2012 Best Student 
Paper Award at the ACM conference on Web Search and Data Mining, the 2010 Facebook 
Fellowship, the 2009 Microsoft Research Fellowship, and several awards for 
entrepreneurial work on social networks.


Refreshments will be served from 14:15
Lecture starts at 14:30