AI News, Simons Institute for the Theory of Computing

Simons Institute for the Theory of Computing

final component of the program is ​understanding heuristics​: what works in practice, and why.  The most popular algorithms for a variety of basic statistical tasks—clustering, embedding, and so on—behave in a manner that is not fully understood.  Some, like principal component analysis, have strong properties, but are used in ways that cannot directly be justified by appealing to these properties.  Others, like k-­means, have obvious failure modes in a worst-­case setting, and yet are quite successful on many types of data.  The program will bring together theoreticians and practitioners who are interested in teasing apart these issues and in expanding the useful formal characterizations of such procedures.   

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