Exact learning of Juntas from membership queries

Nader H. Bshouty, Areej Costa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper we study adaptive and non-adaptive exact learning of Juntas from membership queries. We use new techniques to find new bounds, narrow some of the gaps between the lower bounds and upper bounds and find new deterministic and randomized algorithms with small query and time complexities. Some of the bounds are tight in the sense that finding better ones either gives a breakthrough result in some long-standing combinatorial open problem or needs a new technique that is beyond the existing ones.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 27th International Conference, ALT 2016, Proceedings
EditorsHans Ulrich Simon, Sandra Zilles, Ronald Ortner
Pages115-129
Number of pages15
DOIs
StatePublished - 2016
Event27th International Conference on Algorithmic Learning Theory, ALT 2016 - Bari, Italy
Duration: 19 Oct 201621 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9925 LNAI

Conference

Conference27th International Conference on Algorithmic Learning Theory, ALT 2016
Country/TerritoryItaly
CityBari
Period19/10/1621/10/16

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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