Passing Tests without Memorizing: Two Models for Fooling Discriminators

9 Feb 2019Olivier BousquetRoi LivniShay Moran

We introduce two mathematical frameworks for foolability in the context of generative distribution learning. In a nuthsell, fooling is an algorithmic task in which the input sample is drawn from some target distribution and the goal is to output a synthetic distribution that is indistinguishable from the target w.r.t to some fixed class of tests... (read more)

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