Maria Florina Balcan is the Cadence Design Systems Professor of Computer Science in the School of Computer Science at Carnegie Mellon University. Her main research interests are machine learning, artificial intelligence, theory of computing, and algorithmic game theory. She is a Simons Investigator, a Sloan Fellow, a Microsoft Research New Faculty Fellow, and  recipient of the ACM Grace Murray Hopper Award, an NSF CAREER award, and several best paper awards.   She has co-chaired major conferences in the field: the Conference on Learning Theory (COLT) 2014, the International Conference on Machine Learning (ICML) 2016, and Neural Information Processing Systems (NeurIPS) 2020. She has also  been  the general chair for the International Conference on Machine Learning (ICML) 2021, a board member of the International Machine Learning Society, and a co-organizer for the Simons semester on Foundations of Machine Learning.

AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI   Info

AG Audience

English

60 Minutes

Kaiserslautern

The classic textbook approach to designing and analyzing algorithms for combinatorialproblems considers worst-case instances of the problem, about which the algorithm designer has no prior information. Since for many problems such worst-case guarantees are quite weak, practitioners often employ a data-driven algorithm design approach; specifically,they use machine learning and instances of the problem from their specific domain to learn a method that works well in that domain. Historically, such data-driven algorithmic techniques have come with no performance guarantees. In this talk, I will describeour recent work on providing performance guarantees for data-driven algorithm design both in the distributional and online learning formalizations.

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Geraldine Anderson

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