Communication and Memory Efficient Testing of Discrete Distributions


Ilias Diakonikolas, Themis Gouleakis, Daniel M. Kane, Sankeerth Rao ;
Proceedings of the Thirty-Second Conference on Learning Theory, PMLR 99:1070-1106, 2019.


We study distribution testing with communication and memory constraints in the following computational models: (1) The {\em one-pass streaming model} where the goal is to minimize the sample complexity of the protocol subject to a memory constraint, and (2) A {\em distributed model} where the data samples reside at multiple machines and the goal is to minimize the communication cost of the protocol. In both these models, we provide efficient algorithms for uniformity/identity testing (goodness of fit) and closeness testing (two sample testing). Moreover, we show nearly-tight lower bounds on (1) the sample complexity of any one-pass streaming tester for uniformity, subject to the memory constraint, and (2) the communication cost of any uniformity testing protocol, in a restricted “one-pass” model of communication.

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