Separating models of learning from correlated and uncorrelated data

Journal of Machine Learning Research

Volumn 8, Issue , 2007, Pages 277-290

  Elbaz, Ariel ^a   Lee, Homin K ^a   Servedio, Rocco A ^a   Wan, Andrew ^a  

^a Fu Foundation School of Engineering and Applied Science   (United States)

Author keywords

Correlated data; Cryptographic hardness; PAC learning; Random walks; Uniform distribution learning

Indexed keywords

CORRELATION METHODS; LEARNING ALGORITHMS; MATHEMATICAL MODELS; POLYNOMIAL APPROXIMATION; PUBLIC KEY CRYPTOGRAPHY; RANDOM PROCESSES;

CORRELATED DATA; CRYPTOGRAPHIC HARDNESS; PAC LEARNING; RANDOM WALKS; UNIFORM DISTRIBUTION LEARNING;

LEARNING SYSTEMS;

EID: 33847102836     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (18)

1. D. Aldous and U. Vazirani. A Markovian extension of Valiant's learning model. In Proceedings of the Thirty-First Symposium on Foundations of Computer Science, pages 392-396, 1990.
2. P. Bartlett, P. Fischer, and K.U. Höffgen. Exploiting random walks for learning. Information and Computation, 176(2):121-135, 2002.
3. A. Blum. Learning a function of r relevant variables (open problem). In Proc. 16th Annual COLT, pages 731-733, 2003.
4. A. Blum, M. Furst, J. Jackson, M. Kearns, Y. Mansour, and S. Rudich. Weakly learning DNF and characterizing statistical query learning using Fourier analysis. In Proceedings of the Twenty-Sixth Annual Symposium on Theory of Computing, pages 253-262, 1994.
5. N. Bshouty and C. Tamon. On the Fourier spectrum of monotone functions. Journal of the ACM, 43(4):747-770, 1996.
6. N. Bshouty, J. Jackson, and C. Tamon. More efficient PAC learning of DNF with membership queries under the uniform distribution. In Proceedings of the Twelfth Annual Conference on Computational Learning Theory, pages 286-295, 1999.
7. N. Bshouty, E. Mossel, R. O'Donnell, and R. Servedio. Learning DNF from Random Walks. In Proceedings of the 44th IEEE Symposium on Foundations on Computer Science, pages 189-198, 2003.
8. D. Gamarnik. Extension of the PAC framework to finite and countable Markov chains. In Proceedings of the 12th Annual Conference on Computational Learning Theory, pages 308-317, 1999.
9. O. Goldreich. Foundations of Cryptography: Volume 1, Basic Tools. Cambridge University Press, New York, 2001.
10. O. Goldreich, S. Goldwasser, and S. Micali. How to construct random functions. Journal of the Association for Computing Machinery, 33(4):792-807, 1986.
11. J. Hastad, R. Impagliazzo, L. Levin, and M. Luby. A pseudorandom generator from any one-way function. SIAM Journal on Computing, 28(4):1364-1396, 1999.
12. J. Jackson. An efficient membership-query algorithm for learning DNF with respect to the uniform distribution. Journal of Computer and System Sciences, 55:414-440, 1997.
13. 0. In Proceedings of the 34th ACM Symposium on Theory of Computing, pages 776-784, 2002.
14. M. Kharitonov. Cryptographic hardness of distribution-specific learning. In Proceedings of the Twenty-Fifth Annual Symposium on Theory of Computing, pages 372-381, 1993.
15. N. Linial, Y. Mansour, and N. Nisan. Constant depth circuits, Fourier transform and learnability. Journal of the ACM, 40(3):607-620, 1993.
16. S. Roch. On learning thresholds of parities and unions of rectangles in random walk models. Random Structures and Algorithms, 2006.
17. L. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, 1984.
18. K. Verbeurgt. Learning DNF under the uniform distribution in quasi-polynomial time. In Proceedings of the Third Annual Workshop on Computational Learning Theory, pages 314-326, 1990.