Faster Algorithms for Testing under Conditional Sampling

author: Ananda Theertha Suresh, Department of Computer Science and Engineering, UC San Diego
published: Aug. 20, 2015,   recorded: July 2015,   views: 74
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Description

There has been considerable recent interest in distribution-tests whose run-time and sample requirements are sublinear in the domain-size $k$. We study two of the most important tests under the conditional-sampling model where each query specifies a subset $S$ of the domain, and the response is a sample drawn from $S$ according to the underlying distribution. For identity testing, which asks whether the underlying distribution equals a specific given distribution or $\delta$-differs from it, we reduce the known time and sample complexities from $O(\delta^{-4})$ to $O(\delta^{-2})$, thereby matching the information theoretic lower bound. For closeness testing, which asks whether two distributions underlying observed data sets are equal or different, we reduce existing complexity from $O(\delta^{-4} \log^5 k)$ to an even sub-logarithmic $O(\delta^{-5} \log \log k)$, and providing a better bound to an open problem in Bertinoro Workshop on Sublinear Algorithms.

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Download slides icon Download slides: colt2015_suresh_conditional_sampling_01.pdf (170.7┬áKB)


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