Provable Bounds for Machine Learning: Getting Around Intractability
Sanjeev AroraCharles C. Fitzmorris Professor of Computer Science
Monday, December 02, 2013|
4:30pm - 5:30pm
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About the Event
Many tasks in machine learning (especially unsupervised learning) are
provably intractable: NP-complete or worse. Researchers have developed
heuristic algorithms to try to solve these tasks in
practice. In most cases, these algorithms are heuristics with no
provable guarantees on their running time or on the quality of
solutions they return. Can we change this state of affairs?
Sanjeev Arora is Charles C. Fitzmorris Professor of Computer Science at Princeton University. His research area spans several areas of theoretical Computer Science. He has received the ACM-EATCS Gödel Prize (in 2001 and 2010), Packard Fellowship (1997), the ACM Infosys Foundation Award in the Computing Sciences (2012), the Fulkerson Prize (2012), the Simons Investigator Award (2012). He served as the founding director for the Center for Computational Intractability at Princeton.
Contact: Cindy Estell
Open to: Public