Sharp Bounds for Generalized Uniformity Testing

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart


We study the problem of generalized uniformity testing of a discrete probability distribution: Given samples from a probability distribution p over an unknown size discrete domain Ω, we want to distinguish, with probability at least 2/3, between the case that p is uniform on some subset of Ω versus ε-far, in total variation distance, from any such uniform distribution. We establish tight bounds on the sample complexity of generalized uniformity testing. In more detail, we present a computationally efficient tester whose sample complexity is optimal, within constant factors, and a matching worst-case information-theoretic lower bound. Specifically, we show that the sample complexity of generalized uniformity testing is Θ(1/(ε^(4/3) ||p||3) + 1/(ε^2 ||p||2 )).