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Learning Coverage Functions and Private Release of Marginals
[article]

2014
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arXiv
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pre-print

We study the problem of approximating and learning coverage functions. A function c: 2^[n]→R^+ is a coverage function, if there exists a universe U with non-negative weights w(u) for each u ∈ U and subsets A_1, A_2, ..., A_n of U such that c(S) = ∑_u ∈∪_i ∈ S A_i w(u). Alternatively, coverage functions can be described as non-negative linear combinations of monotone disjunctions. They are a natural subclass of submodular functions and arise in a number of applications. We give an algorithm that

arXiv:1304.2079v3
fatcat:lor5pjhisfd4vo534nxcwml2im