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Error-Tolerant Non-Adaptive Learning of a Hidden Hypergraph

Author Hasan Abasi

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Hasan Abasi
  • Department of Computer Science, Technion, Haifa, 32000, Israel

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Hasan Abasi. Error-Tolerant Non-Adaptive Learning of a Hidden Hypergraph. In 43rd International Symposium on Mathematical Foundations of Computer Science (MFCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 117, pp. 3:1-3:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)
https://doi.org/10.4230/LIPIcs.MFCS.2018.3

Abstract

We consider the problem of learning the hypergraph using edge-detecting queries. In this model, the learner is allowed to query whether a set of vertices includes an edge from a hidden hypergraph. Except a few, all previous algorithms assume that a query's result is always correct. In this paper we study the problem of learning a hypergraph where alpha -fraction of the queries are incorrect. The main contribution of this paper is generalizing the well-known structure CFF (Cover Free Family) to be Dense (we will call it DCFF - Dense Cover Free Family) while presenting three different constructions for DCFF. Later, we use these constructions wisely to give a polynomial time non-adaptive learning algorithm for a hypergraph problem with at most alpha-fracion incorrect queries. The hypergraph problem is also known as both monotone DNF learning problem, and complexes group testing problem.

Subject Classification

ACM Subject Classification
  • Theory of computation → Boolean function learning
Keywords
  • Error Tolerant Algorithm
  • Hidden Hypergraph
  • Montone DNF
  • Group Testing
  • Non-Adaptive Learning

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References

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