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Lower Bounds on Learning Random Structures with Statistical Queries

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Algorithmic Learning Theory (ALT 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6331))

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Abstract

We show that random DNF formulas, random log-depth decision trees and random deterministic finite acceptors cannot be weakly learned with a polynomial number of statistical queries with respect to an arbitrary distribution on examples.

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Author information

Authors and Affiliations

  1. Yale University, New Haven, CT, USA

    Dana Angluin

  2. Brown University, Providence, RI, USA

    David Eisenstat

  3. Ben Gurion University of the Negev, Israel

    Leonid (Aryeh) Kontorovich

  4. Yahoo! Research, New York, NY, USA

    Lev Reyzin

Authors
  1. Dana Angluin
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  2. David Eisenstat
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  3. Leonid (Aryeh) Kontorovich
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  4. Lev Reyzin
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Editor information

Editors and Affiliations

  1. Research School of Information Sciences and Engineering, Australian National University and NICTA, 0200, Canberra, ACT, Australia

    Marcus Hutter

  2. Department of Mathematics, National University of Singapore, Block S17, 10 Lower Kent Ridge Road, 119076, Singapore, Republic of Singapore

    Frank Stephan

  3. Department of Computer Science, University of London, Royal Holloway, TW20 0EX, Egham, Surrey, UK

    Vladimir Vovk

  4. Division of Computer Science, Hokkaido University, , ,, N-14, W-9, Sapporo, 060-0814, Japan

    Thomas Zeugmann

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© 2010 Springer-Verlag Berlin Heidelberg

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Angluin, D., Eisenstat, D., Kontorovich, L.(., Reyzin, L. (2010). Lower Bounds on Learning Random Structures with Statistical Queries. In: Hutter, M., Stephan, F., Vovk, V., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2010. Lecture Notes in Computer Science(), vol 6331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16108-7_18

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  • DOI: https://doi.org/10.1007/978-3-642-16108-7_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16107-0

  • Online ISBN: 978-3-642-16108-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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