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Abstract: We consider a fundamental problem in computational learning theory: learning an arbitrary Boolean function which depends on an unknown set of k out of n Boolean variables. We give an algorithm for learning such functions from uniform random examples which runs in time roughly (n , where # < 2.376 is the matrix multiplication exponent. We thus obtain the first polynomial factor improvement on the naive n time bound which can be achieved via exhaustive search. Our algorithm and... (Update)
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Computational Applications of Noise Sensitivity - O'Donnell (2003) (Correct)
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BibTeX entry: (Update)
E. Mossel, R. O'Donnell, and R. Servedio. Learning juntas. In Proc. 35th Ann. ACM Symp. on the Theory of Computing, 2003. http://citeseer.ist.psu.edu/article/mossel03learning.html More
@misc{ mossel03learning,
author = "E. Mossel and R. O'Donnell and R. Servedio",
title = "Learning juntas",
text = "E. Mossel, R. O'Donnell, and R. Servedio. Learning juntas. In Proc. 35th
Ann. ACM Symp. on the Theory of Computing, 2003.",
year = "2003",
url = "citeseer.ist.psu.edu/article/mossel03learning.html" }
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2 a hierarchy of boolean functions hard to compute in constant.. (context) - Bernasconi - 2001
2 Personal communication (context) - Regev - 2003
Documents on the same site (http://math.ias.edu/~odonnell/): More
Computational Applications of Noise Sensitivity - O'Donnell (2003) (Correct)
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Learning DNF from Random Walks - Bshouty, Mossel, O'Donnell, Servedio (2003) (Correct)
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