LIPIcs.CCC.2019.2.pdf
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Almost Optimal Distribution-Free Junta Testing
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34th Computational Complexity Conference (CCC 2019)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: Computational Complexity Conference (CCC) - License:
Creative Commons Attribution 3.0 Unported license
- Publication date: 2019-07-16
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Acknowledgements
We would like to thank Xi Chen for reading the early version of the paper and for verifying the correctness of the algorithm.
Cite AsGet BibTex
Nader H. Bshouty. Almost Optimal Distribution-Free Junta Testing. In 34th Computational Complexity Conference (CCC 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 137, pp. 2:1-2:13, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.CCC.2019.2
https://doi.org/10.4230/LIPIcs.CCC.2019.2
Abstract
We consider the problem of testing whether an unknown n-variable Boolean function is a k-junta in the distribution-free property testing model, where the distance between functions is measured with respect to an arbitrary and unknown probability distribution over {0,1}^n. Chen, Liu, Servedio, Sheng and Xie [Zhengyang Liu et al., 2018] showed that the distribution-free k-junta testing can be performed, with one-sided error, by an adaptive algorithm that makes O~(k^2)/epsilon queries. In this paper, we give a simple two-sided error adaptive algorithm that makes O~(k/epsilon) queries.
Subject Classification
ACM Subject Classification
- Mathematics of computing
- Mathematics of computing → Discrete mathematics
- Mathematics of computing → Probabilistic algorithms
- Theory of computation → Probabilistic computation
Keywords
- Distribution-free property testing
- k-Junta
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