Near-Optimal Active Learning of Halfspaces via Query Synthesis in the Noisy Setting

31st AAAI Conference on Artificial Intelligence, AAAI 2017

Volumn , Issue , 2017, Pages 1798-1804

  Chen, Lin ^a,b   Hassani, Hamed ^c   Karbasi, Amin ^a,b  

^a Department of Electrical Engineering ^*  (United States)

^b YALE UNIVERSITY   (United States)

^c ETH ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COST ENGINEERING; HEURISTIC ALGORITHMS; OPTIMIZATION; REVERSE ENGINEERING;

CONSTANT FACTORS; CURSE OF DIMENSIONALITY; DECISION BOUNDARY; LEARNING PROBLEM; LINEAR CLASSIFIERS; ORDERS OF MAGNITUDE; QUERY COMPLEXITY; THEORETICAL GUARANTEES;

QUERY PROCESSING;

EID: 85030477094     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. Abe, N., and Mamitsuka, H. 1998. Query learning strategies using boosting and bagging. In ICML, Volume 1.
2. Alabdulmohsin, I.; Gao, X.; and Zhang, X. 2015. Efficient active learning of halfspaces via query synthesis. In AAAI, 2483-2489.
3. Angluin, D. 1988. Queries and concept learning. Machine learning 2(4):319-342.
4. Awasthi, P.; Balcan, M. F.; and Long, P. M. 2014. The power of localization for efficiently learning linear separators with noise. In STOC, 449-458. ACM.
5. Balcan, M.-F., and Long, P. M. 2013. Active and passive learning of linear separators under log-concave distributions. In COLT, 288-316.
6. Balcan, M.-F.; Beygelzimer, A.; and Langford, J. 2006. Agnostic active learning. In ICML, 65-72. ACM.
7. Balcan, M.-F.; Broder, A.; and Zhang, T. 2007. Margin based active learning. In Learning Theory. Springer. 35-50.
8. Chen, Y.; Hassani, S. H.; Karbasi, A.; and Krause, A. 2015. Sequential information maximization: When is greedy near-optimal? In COLT, Volume 40.
9. Chen, L.; Hassani, H.; and Karbasi, A. 2016. Near-optimal active learning of halfspaces via query synthesis in the noisy setting. arXiv preprint arXiv:1603.03515. https://arxiv.org/abs/1603.03515.
10. Cohn, D. A.; Ghahramani, Z.; and Jordan, M. I. 1996. Active learning with statistical models. JAIR.
11. Dasgupta, S., and Hsu, D. 2008. Hierarchical sampling for active learning. In ICML, 208-215. ACM.
12. Dasgupta, S.; Kalai, A. T.; and Monteleoni, C. 2005. Analysis of perceptron-based active learning. In COLT, 249-263.
13. Dasgupta, S.; Kalai, A. T.; and Monteleoni, C. 2009. Analysis of perceptron-based active learning. JMLR 10(Feb):281-299.
14. Dasgupta, S. 2004. Analysis of a greedy active learning strategy. In NIPS, 337-344.
15. Freund, Y.; Seung, H. S.; Shamir, E.; and Tishby, N. 1997. Selective sampling using the query by committee algorithm. Machine learning 28(2-3):133-168.
16. Hakkani-Tur, D.; Riccardi, G.; and Gorin, A. 2002. Active learning for automatic speech recognition. In ICASSP, Volume 4, IV-3904. IEEE.
17. Javdani, S.; Chen, Y.; Karbasi, A.; Krause, A.; Bagnell, J. A.; and Srinivasa, S. 2014. Near optimal bayesian active learning for decision making. In AISTAT, 430-438.
18. Kääriäinen, M. 2006. Active learning in the non-realizable case. In ALT, 63-77. Springer.
19. Karbasi, A.; Ioannidis, S.; and Massoulie, L. 2012. Comparison-based learning with rank nets. ICML.
20. Karp, R. M., and Kleinberg, R. 2007. Noisy binary search and its applications. In SODA, 881-890.
21. King, R. D.; Rowland, J.; Oliver, S. G.; Young, M.; Aubrey, W.; Byrne, E.; Liakata, M.; Markham, M.; Pir, P.; Soldatova, L. N.; et al. 2009. The automation of science. Science 324(5923):85-89.
22. Lewis, D. D., and Gale, W. A. 1994. A sequential algorithm for training text classifiers. In SIGIR, 3-12.
23. Lowd, D., and Meek, C. 2005. Adversarial learning. In KDD, 641-647.
24. Nelson, B.; Rubinstein, B. I.; Huang, L.; Joseph, A. D.; Lee, S. J.; Rao, S.; and Tygar, J. 2012. Query strategies for evading convex-inducing classifiers. JMLR 13(May):1293-1332.
25. Nowak, R. D. 2011. The geometry of generalized binary search. IEEE Transactions on Information Theory 57(12):7893-7906.
26. Settles, B. 2010. Active learning literature survey. University of Wisconsin, Madison 52(55-66):11.
27. Shawe-Taylor, J., and Cristianini, N. 2004. Kernel methods for pattern analysis. Cambridge university press.
28. Tong, S., and Chang, E. 2001. Support vector machine active learning for image retrieval. In Proc. 9th ACM international conference on Multimedia, 107-118.
29. Tong, S., and Koller, D. 2002. Support vector machine active learning with applications to text classification. JMLR 2:45-66.
30. Valiant, L. G. 1984. A theory of the learnable. Communications of the ACM 27(11):1134-1142.