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Non-adaptive Learning of a Hidden Hypergraph

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Algorithmic Learning Theory (ALT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9355))

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Abstract

We give a new deterministic algorithm that non-adaptively learns a hidden hypergraph from edge-detecting queries. All previous non-adaptive algorithms either run in exponential time or have non-optimal query complexity. We give the first polynomial time non-adaptive learning algorithm for learning hypergraph that asks an almost optimal number of queries.

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Author information

Authors and Affiliations

  1. Department of Computer Science Technion, 32000, Haifa, Israel

    Hasan Abasi & Nader H. Bshouty

  2. Google, 76 Buckingham Palace Rd, London, USA

    Hanna Mazzawi

Authors
  1. Hasan Abasi
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  2. Nader H. Bshouty
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  3. Hanna Mazzawi
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Corresponding author

Correspondence to Nader H. Bshouty .

Editor information

Editors and Affiliations

  1. University of California, La Jolla, California, USA

    Kamalika Chaudhuri

  2. UNIV DEL INSUBRIA, 21100 VARESE, Italy

    CLAUDIO GENTILE

  3. REGINA, Saskatchewan, Canada

    Sandra Zilles

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Abasi, H., Bshouty, N.H., Mazzawi, H. (2015). Non-adaptive Learning of a Hidden Hypergraph. In: Chaudhuri, K., GENTILE, C., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2015. Lecture Notes in Computer Science(), vol 9355. Springer, Cham. https://doi.org/10.1007/978-3-319-24486-0_6

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  • DOI: https://doi.org/10.1007/978-3-319-24486-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24485-3

  • Online ISBN: 978-3-319-24486-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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