On learning bounded-width branching programs

Funda Ergün, S. Ravi Kumar, Ronitt Rubinfeld

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

Abstract

In this paper, we study PAC-learning algorithms for specialized classes of deterministic finite automata (DFA). In particular, we study branching programs, and we investigate the influence of the width of the branching program on the difficulty of the learning problem. We first present a distribution-free algorithm for learning width-2 branching programs. We also give an algorithm for the proper learning of width-2 branching programs under uniform distribution on labeled samples. We then show that the existence of an efficient algorithm for learning width-3 branching programs would imply the existence of an efficient algorithm for learning DNF, which is not known to be the case. Finally, we show that the existence of an algorithm for learning width-3 branching programs would also yield an algorithm for learning a very restricted version of parity with noise.

Original languageEnglish
Title of host publicationProceedings of the 8th Annual Conference on Computational Learning Theory, COLT 1995
PublisherAssociation for Computing Machinery, Inc
Pages361-368
Number of pages8
ISBN (Electronic)0897917235, 9780897917230
DOIs
StatePublished - 5 Jul 1995
Externally publishedYes
Event8th Annual Conference on Computational Learning Theory, COLT 1995 - Santa Cruz, United States
Duration: 5 Jul 19958 Jul 1995

Publication series

NameProceedings of the 8th Annual Conference on Computational Learning Theory, COLT 1995
Volume1995-January

Conference

Conference8th Annual Conference on Computational Learning Theory, COLT 1995
Country/TerritoryUnited States
CitySanta Cruz
Period5/07/958/07/95

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