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You Music next up we have Nina from CMU talking about data-driven algorithm design yeah of course so I'll be talking about using machine learning for data-driven automated algorithm design and in particular about some recent provable grantees for this I think very exciting area of data-driven or alwasy design okay and my focus in the talk will be on combinatorial problem so in discrete structures the community algorithms or discrete structures so the way I think about it is like using machine learning Alani theory to influence theory of computing and algorithm design and hopefully the corresponding application areas okay and let me start with some very high-level motivation for this line of work so the classic way to design and analyze algorithms for comunitaria problems impose at the ovens that we designed for a given problem will be used to solve worst case instance isn't the problem about which the algorithm has absolutely no information at all ok and so in this classy framework the kind of performance guarantees we seek for for our algorithm is that the algorithm will succeed even for sorting the one time worst case the instance of the underlying algorithm problem ok and so within this classic framework you know well know there are some products that are easy so for example if we are concerned the running time is our major performance measure we know there are some problems that we teaching basic undergraduate algorithms courses for which we have optimal polynomial time algorithms but I'll guarantee to output the optimal solution in polynomial time even worse case instances ok so there are some brands that are either within the classic framework but if but for most problems within the classic framework we do not have such strong performance guarantees ok so examples of hard problems include clustering problem problems partitioning problem subset selection problems in fact actually most combinatorial problems are kind of hard with in the classic framework now one interesting approach commonly used in practice for had problems especially in cases when it you need to solve not only one instance of the underlying economy problem but cases already to repeatedly solve instances of the underlying algorithmic problem so one kind of interesting approach used in practiced over to solve these hard problems is to use machine learning and instances coming from the given domain in order to learn an algorithm but as well just in our type of instances okay and this is very natural idea of course because you know in practice different math for a given type of combinatorial problem different methods or better in different settings and also often we often have large family of methods that we could choose from so it's very natural to try to use machine learning and instances of the problem coming from our specific domain to learn an algorithm but as well just in our type of instances okay and indeed this approach has a large history in many applied communities...
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