Wednesday, February 15, 2017
12:00 PM -
1:00 PM
Annenberg 213
RSRG Seminar
Some Limitations and Possibilities Toward Data-driven Optimization
Yaron Singer,
Professor,
Computer Science,
Harvard University,
Speaker's 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, and the 2009 Microsoft Research Fellowship.
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, and the 2009 Microsoft Research Fellowship.
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. 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 inapproximability results. In this talk we will formalize some of these ideas using convex and combinatorial optimization.
Event Sponsors:
For more information, please contact Sheila Shull by phone at 626.395.4560 or by email at [email protected].