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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
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specific domain to learn a method that works best in that domain. Historically,
such algorithmic techniques have come with no performance guarantees. In this
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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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HUMAN INSPIRED ARTIFICIAL INTELLIGENCE WITH MANISH GUPTA

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QUANTUM COMPUTATIONAL SUPREMACY WITH SCOTT AARONSON

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RUNNING LINUX APPS ON WINDOWS: HOW AND WHY? WITH SCOTT HANSELMAN


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