Learning from a consistently ignorant teacher

Michael Frazier, Sally Goldman, Nina Mishra, Leonard Pitt

Research output: Chapter in Book/Report/Conference proceedingConference contribution


One view of computational learning theory is that of a learner acquiring the knowledge of a teacher. We introduce a formal model of learning capturing the idea that teachers may have gaps in their knowledge. The goal of the learner is still to acquire the knowledge of the teacher, but now the learner must also identify the gaps. This is the notion of learning from a consistently ignorant teacher. We consider the impact of knowledge gaps on learning, for example, monotone DNF and d-dimensional boxes, and show that learning is still possible. Negatively, we show that knowledge gaps make learning conjunctions of Horn clauses as hard as learning DNF. We also present general results describing when known learning algorithms can be used to obtain learning algorithms using a consistently ignorant teacher.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th Annual Conference on Computational Learning Theory, COLT 1994
PublisherAssociation for Computing Machinery
Number of pages12
ISBN (Electronic)0897916557
StatePublished - Jul 16 1994
Externally publishedYes
Event7th Annual Conference on Computational Learning Theory, COLT 1994 - New Brunswick, United States
Duration: Jul 12 1994Jul 15 1994

Publication series

NameProceedings of the Annual ACM Conference on Computational Learning Theory
VolumePart F129415


Other7th Annual Conference on Computational Learning Theory, COLT 1994
Country/TerritoryUnited States
CityNew Brunswick

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence


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