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Machine Learning for Algorithm Design with Maria Florina Balcan
The classic textbook approach to designing and analyzing algorithms assumes worst-case instances of the problem, about which the algorithm designer has absolutely no information at all. Unfortunately, for many problems such worst-case guarantees—either for solution quality or running time or other performance measures—are often weak. Consequently, rather than using off the shelf algorithms that have weak worst-case guarantees, practitioners often employ a data-driven algorithm design approach; specifically, given an application, they use machine learning and instances of the problem from the specific domain to learn a method that works best in that domain. Historically, such algorithmic techniques have come with no performance guarantees. In this talk, I will describe our recent work that helps put data-driven algorithm design on firm foundations. I will describe both specific case studies and general principles applicable broadly to a variety of combinatorial problems.
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Register now for the free ACM TechTalk, "The Essence of Software (Or Why Systems Often Fail by Design, and How to Fix Them)", presented on Wednesday, December 1 at 12:00 PM ET/9:00 AM PT, by Daniel Jackson, ACM Fellow and Professor of Computer Science at MIT. George Fairbanks, Software Engineer at Google, will moderate the qestions and answers session following the talk. Continue the discussion on ACM's Discourse Page.
View the recent ACM TechTalk, "Getting Started with Kubernetes," presented by Nigel Poulton, Kubernetes and Docker trainer and author. Dominic Holt, CEO of Harpoon Corp and a member of the ACM Professional Development Committee, moderated the qestions and answers session following the talk. Continue the discussion on ACM's Discourse Page.