Almost Optimal Distribution-free Junta Testing
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Abstract (arXiv)
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 function is measured with respect to an arbitrary and unknown probability distribution over 0,1n. Chen, Liu, Servedio, Sheng and Xie showed that the distribution-free k-junta testing can be performed, with one-sided error, by an adaptive algorithm that makes tilde O(k2)/ε queries. In this paper, we give a simple two-sided error adaptive algorithm that makes tilde O(k/ε) queries.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jan 2019.
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.
- 2022Superpolynomial Lower Bounds for Decision Tree Learning and Testingcited by 9
- 2021On Learning and Testing Decision Treecited by 0
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- 2021On Learning and Testing Decision Tree10 Aug 2021