Synthetic Data Generators -- Sequential and Private

NIPS 2020, 2020.

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The following statements are equivalent for a class D ⊆ {0, 1}X: 1

Abstract:

We study the sample complexity of private synthetic data generation over an unbounded sized class of statistical queries, and show that any class that is privately proper PAC learnable admits a private synthetic data generator (perhaps nonefficient). Previous work on synthetic data generators focused on the case that the query class D is ...More

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