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Agnostic Learning from Tolerant Natural Proofs

2017
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International Workshop on Approximation Algorithms for Combinatorial Optimization
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We generalize the "learning algorithms from natural properties" framework of [4] to get agnostic learning algorithms from natural properties with extra features. We show that if a natural property (in the sense of Razborov and Rudich [28] ) is useful also against functions that are close to the class of "easy" functions, rather than just against "easy" functions, then it can be used to get an agnostic learning algorithm over the uniform distribution with membership queries. For AC 0 [q], any

doi:10.4230/lipics.approx-random.2017.35
dblp:conf/approx/CarmosinoIKK17
fatcat:vewhdm7sjzhsjmn2ljblst2ezi