On the Learnability of Shuffle Ideals
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Angluin_On-the-learnability.pdf
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173.13 KB
Format
Adobe PDF
Checksum (MD5)
5019cf1691ce6f5918b112f9a821552d
Author(s)
Eisenstat, Sarah Charmian
Angluin, Dana
Aspnes, James
Kontorovich, Aryeh
Date Issued
June 2013
Journal
Journal of Machine Learning Research
Publisher
Association for Computing Machinery (ACM)
Citation
Angluin, Dana et al. “On the Learnability of Shuffle Ideals.” Journal of Machine Learning Research 14 (2013): 1513–1531.
Version
Final published version
Abstract
PAC learning of unrestricted regular languages is long known to be a difficult problem. The class of shuffle ideals is a very restricted subclass of regular languages, where the shuffle ideal generated by a string u is the collection of all strings containing u as a subsequence. This fundamental language family is of theoretical interest in its own right and provides the building blocks for other important language families. Despite its apparent simplicity, the class of shuffle ideals appears quite difficult to learn. In particular, just as for unrestricted regular languages, the class is not properly PAC learnable in polynomial time if RP 6= NP, and PAC learning the class improperly in polynomial time would imply polynomial time algorithms for certain fundamental problems in cryptography. In the positive direction, we give an efficient algorithm for properly learning shuffle ideals in the statistical query (and therefore also PAC) model under the uniform distribution.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://jmlr.org/papers/volume14/angluin13a/angluin13a.pdf
