Property testing and its connection to learning and approximation

@article{Goldreich1996PropertyTA,
  title={Property testing and its connection to learning and approximation},
  author={Oded Goldreich and Shafi Goldwasser and Dana Ron},
  journal={Proceedings of 37th Conference on Foundations of Computer Science},
  year={1996},
  pages={339-348}
}
The authors study the question of determining whether an unknown function has a particular property or is /spl epsiv/-far from any function with that property. A property testing algorithm is given a sample of the value of the function on instances drawn according to some distribution, and possibly may query the function on instances of its choice. First, they establish some connections between property testing and problems in learning theory. Next, they focus on testing graph properties, and… 

Property Testing: A Learning Theory Perspective

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  • Computer Science
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  • 2020
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Combinatorial property testing (a survey)

  • Oded Goldreich
  • Mathematics
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  • 1997
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  • Oded GoldreichL. Trevisan
  • Mathematics, Computer Science
    Proceedings 2001 IEEE International Conference on Cluster Computing
  • 2001
Three theorems regarding testing graph properties in the adjacency matrix representation are presented and every graph property that can be tested making a number of queries that is independent of the size of the graph, can be so tested by uniformly selecting a set of vertices.

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  • D. Ron
  • Computer Science, Mathematics
    Found. Trends Theor. Comput. Sci.
  • 2009
This monograph surveys results in property testing, where the emphasis is on common analysis and algorithmic techniques.

Active Property Testing

A general notion of the testing dimension of a given property with respect to a given distribution is developed, that characterizes (up to constant factors) the intrinsic number of label requests needed to test that property.

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Testing Eulerianity and connectivity in directed sparse graphs

On the Testability of Graph Partition Properties

This work studies the testability of a family of graph partition properties that generalizes a family previously studied by Goldreich, Goldwasser, and Ron and shows that every property in GPP is testable by a one-sided error algorithm that has query complexity poly(1/ ) and that if P ∈ GPP\GPP0,1 then it cannot have aOne- sided error testing algorithm whose query complexity is independent of the input graph's size.
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