Establishes Ω(n/ε²) query lower bounds for approximating correlation clustering cost and partitions under memory constraints in adjacency-matrix and general graph models.
Property testing and its connection to learning and approximation.J
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k-juntas, low-degree Fourier functions, and sparse polynomials are testable with O(1/ε) queries independent of n for small ε.
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Query Lower Bounds for Correlation Clustering under Memory Constraints
Establishes Ω(n/ε²) query lower bounds for approximating correlation clustering cost and partitions under memory constraints in adjacency-matrix and general graph models.
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Classes Testable with $O(1/\epsilon)$ Queries for Small $\epsilon$ Independent of the Number of Variables
k-juntas, low-degree Fourier functions, and sparse polynomials are testable with O(1/ε) queries independent of n for small ε.