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Wednesday, May 15, 2019
Cornell Tech, Bloomberg Center, Room TBD 2 West Loop Road New York, NY 10044
"Algorithms in the Era of Machine Learning: An Inconvenient Truth"
The traditional approach in algorithm design assumes that there is an underlying objective that is known to the algorithm designer and the focus is on efficiently optimizing that objective. In many applications however, the objectives we aim to optimize are not known but rather learned from data. So what are the guarantees of the algorithms we develop and teach when the input is learned from data? In this talk we will address this question and discuss challenges at the intersection of machine learning and algorithms. We will present some stark impossibility results and argue for new algorithmic paradigms.
Yaron Singer is an Associate 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, the Sloan fellowship, Facebook faculty award, Google faculty award, 2012 Best Student Paper Award at the ACM conference on Web Search and Data Mining, the 2010 Facebook Graduate Fellowship, the 2009 Microsoft Research PhD Fellowship.