Statistical active learning algorithms

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Balcan, Maria Florina ^a   Feldman, Vitaly ^b  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^b IBM ALMADEN RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

TEXT PROCESSING;

ACTIVE-LEARNING ALGORITHM; CONCEPT CLASS; LINEAR SEPARATORS; STATISTICAL LEARNING; STATISTICAL QUERIES;

LEARNING ALGORITHMS;

EID: 84898988307     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. D. Angluin and P. Laird. Learning from noisy examples. Machine Learning, 2:343-370, 1988.
2. J. Aslam and S. Decatur. Specification and simulation of statistical query algorithms for efficiency and noise tolerance. JCSS, 56:191-208, 1998.
3. M. Balcan, A. Broder, and T. Zhang. Margin based active learning. In COLT, pages 35-50, 2007.
4. M. F. Balcan, A. Beygelzimer, and J. Langford. Agnostic active learning. In ICML, 2006.
5. M. F. Balcan and V. Feldman. Statistical active learning algorithms, 2013. ArXiv:1307.3102.
6. M.-F. Balcan and S. Hanneke. Robust interactive learning. In COLT, 2012.
7. M.-F. Balcan, S. Hanneke, and J. Wortman. The true sample complexity of active learning. In COLT, 2008.
8. M.-F. Balcan and P. M. Long. Active and passive learning of linear separators under log-concave distributions. JMLR - COLT proceedings (to appear), 2013.
9. A. Beygelzimer, S. Dasgupta, and J. Langford. Importance weighted active learning. In ICML, pages 49-56, 2009.
10. A. Beygelzimer, D. Hsu, J. Langford, and T. Zhang. Agnostic active learning without constraints. In NIPS, 2010.
11. A. Blum, C. Dwork, F. McSherry, and K. Nissim. Practical privacy: the SuLQ framework. In Proceedings of PODS, pages 128-138, 2005.
12. A. Blum, A. Frieze, R. Kannan, and S. Vempala. A polynomial time algorithm for learning noisy linear threshold functions. Algorithmica, 22(1/2):35-52, 1997.
13. 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 STOC, pages 253-262, 1994.
14. A. Blumer, A. Ehrenfeucht, D. Haussler, and M. Warmuth. Learnability and the Vapnik-Chervonenkis dimension. Journal of the ACM, 36(4):929-965, 1989.
15. R. Castro and R. Nowak. Minimax bounds for active learning. In COLT, 2007.
16. N. Cesa-Bianchi, C. Gentile, and L. Zaniboni. Learning noisy linear classifiers via adaptive and selective sampling. Machine Learning, 2010.
17. C. Chu, S. Kim, Y. Lin, Y. Yu, G. Bradski, A. Ng, and K. Olukotun. Map-reduce for machine learning on multicore. In Proceedings of NIPS, pages 281-288, 2006.
18. S. Dasgupta. Coarse sample complexity bounds for active learning. In NIPS, volume 18, 2005.
19. S. Dasgupta. Active learning. Encyclopedia of Machine Learning, 2011.
20. S. Dasgupta and D. Hsu. Hierarchical sampling for active learning. In ICML, pages 208-215, 2008.
21. S. Dasgupta, D.J. Hsu, and C. Monteleoni. A general agnostic active learning algorithm. NIPS, 20, 2007.
22. S. Dasgupta, A. Tauman Kalai, and C. Monteleoni. Analysis of perceptron-based active learning. Journal of Machine Learning Research, 10:281-299, 2009.
23. O. Dekel, C. Gentile, and K. Sridharan. Selective sampling and active learning from single and multiple teachers. JMLR, 2012.
24. J. Dunagan and S. Vempala. A simple polynomial-time rescaling algorithm for solving linear programs. In STOC, pages 315-320, 2004.
25. C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In TCC, pages 265-284, 2006.
26. V. Feldman. A complete characterization of statistical query learning with applications to evolvability. Journal of Computer System Sciences, 78(5):1444-1459, 2012.
27. Y. Freund, H.S. Seung, E. Shamir, and N. Tishby. Selective sampling using the query by committee algorithm. Machine Learning, 28(2-3):133-168, 1997.
28. A. Gonen, S. Sabato, and S. Shalev-Shwartz. Efficient pool-based active learning of halfspaces. In ICML, 2013.
29. S. Hanneke. A bound on the label complexity of agnostic active learning. In ICML, 2007.
30. M. Kearns. Efficient noise-tolerant learning from statistical queries. JACM, 45(6):983-1006, 1998.
31. V. Koltchinskii. Rademacher complexities and bounding the excess risk in active learning. JMLR, 11:2457-2485, 2010.
32. L. Lovász and S. Vempala. The geometry of logconcave functions and sampling algorithms. Random Struct. Algorithms, 30(3):307-358, 2007.
33. A. McCallum and K. Nigam. Employing EM in pool-based active learning for text classification. In ICML, pages 350-358, 1998.
34. M. Raginsky and A. Rakhlin. Lower bounds for passive and active learning. In NIPS, pages 1026-1034, 2011.
35. L. G. Valiant. A theory of the learnable. Communications of the ACM, 27(11):1134-1142, 1984.