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Amir Zandieh
Efficiently Learning Fourier Sparse Set Functions
Andisheh Amrollahi, Amir Zandieh, Michael Kapralov, Andreas Krause
Neural Information Processing Systems http://nips.cc/
Efficiently Learning Fourier Sparse Set Functions
Andisheh Amrollahi, Amir Zandieh, Michael Kapralov, Andreas Krause
Learning set functions is a key challenge arising in many domains, ranging from sketching graphs to black-box optimization with discrete parameters. In this paper we consider the problem of efficiently learning set functions that are defined over a ground set of size n and that are sparse (say k-sparse) in the Fourier domain. This is a wide class, that includes graph and hypergraph cut functions, decision trees and more.