Learning using local membership queries

Pranjal Awasthi, Vitaly Feldman, Varun Kanade

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


We introduce a new model of membership query (MQ) learning, where the learning algorithm is restricted to query points that are close to random examples drawn from the underlying distribution. The learning model is intermediate between the PAC model (Valiant, 1984) and the PAC+MQ model (where the queries are allowed to be arbitrary points). Membership query algorithms are not popular among machine learning practitioners. Apart from the obvious difficulty of adaptively querying labelers, it has also been observed that querying unnatural points leads to increased noise from human labelers (Lang and Baum, 1992). This motivates our study of learning algorithms that make queries that are close to examples generated from the data distribution. We restrict our attention to functions defined on the n-dimensional Boolean hypercube and say that a membership query is local if its Hamming distance from some example in the (random) training data is at most O(log(n)). We show the following results in this model: (i) The class of sparse polynomials (with coefficients in ℝ) over {0, 1}n is polynomial time learnable under a large class of log-Lipschitz distributions using O(log(n))-local queries. This class also includes the class of O(log(n))-depth decision trees. (ii) The class of polynomial-sized decision trees is polynomial time learnable under product distributions using O(log(n))-local queries. (iii) The class of polynomial size DNF formulas is learnable under the uniform distribution using O(log(n))-local queries in time nO(log(log(n))). (iv) In addition we prove a number of results relating the proposed model to the traditional PAC model and the PAC+MQ model.

Original languageEnglish (US)
Pages (from-to)398-431
Number of pages34
JournalJournal of Machine Learning Research
StatePublished - 2013
Externally publishedYes
Event26th Conference on Learning Theory, COLT 2013 - Princeton, NJ, United States
Duration: Jun 12 2013Jun 14 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence


  • DNF
  • Decision trees
  • Membership queries
  • PAC learning


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