Almost Optimal Testers for Concise Representations
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Abstract (arXiv)
We give improved and almost optimal testers for several classes of Boolean functions on n inputs that have concise representation in the uniform and distribution-free model. Classes, such as k-junta, k-linear functions, s-term DNF, s-term monotone DNF, r-DNF, decision list, r-decision list, size-s decision tree, size-s Boolean formula, size-s branching programs, s-sparse polynomials over the binary field and function with Fourier degree at most d. The method can be extended to several other classes of functions over any domain that can be approximated by functions that have a small number of relevant variables.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Apr 2019.
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.
- 2021On Learning and Testing Decision Treecited by 0
- 2022Almost Optimal Proper Learning and Testing Polynomialscited by 0
- 2022Almost Optimal Proper Learning and Testing Polynomials7 Feb 2022
- 2021On Learning and Testing Decision Tree10 Aug 2021