Good learners for evil teachers

ACM International Conference Proceeding Series

Volumn 382, Issue , 2009, Pages

  Dekel, Ofer ^a   Shamir, Ohad ^b  

^a MICROSOFT RESEARCH   (United States)

^b HEBREW UNIVERSITY OF JERUSALEM   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

EMPIRICAL RESULTS; FORMAL ANALYSIS; LEARNING PROCESS; LOW QUALITIES; PRIOR INFORMATION; SUPERVISED MACHINE LEARNING;

ROBOT LEARNING; TEACHING;

LEARNING ALGORITHMS;

EID: 70049097966     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1553374.1553404     Document Type: Conference Paper

References (11)

1. Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Wortman, J. (2007). Learning bounds for domain adaptation. Advances in Neural Information Processing Systems 21 (pp. 129-136).
2. Crammer, K., Kearns, M., & Wortman, J. (2008). Learning from multiple sources. Journal of Machine Learning Research, 9, 1757-1774.
3. Dekel, O., Fischer, F., & Procaccia, A. D. (2008). Incentive compatible regression learning. Proceedings of the 19th Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 884-893).
4. Dekel, O., & Shamir, O. (2008). Learning to classify with missing and corrupted features. Proceedings of the 25th International Conference on Machine Learning (pp. 216-223).
5. Kearns, M. J. (1998). Efficient noise-tolerant learning from statistical queries. Journal of the ACM, 45, 983-1006.
6. Lewis, D., Yang, Y., Rose, T., & Li, F. (2004). RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5, 361-397.
7. Shalev-Shwartz, S., Singer, Y., & Srebro, N. (2007). Pegasos: Primal estimated sub-gradient solver for svm. Proceedings of the 24th International Conference on Machine Learning (pp. 807-814).
8. Shawe-Taylor, J., & Cristianini, N. (2000). Support vector machines and other kernel-based learning methods. Cambridge University Press.
9. Sheng, V., Provost, F., & Ipeirotis, P. (2008). Get another label? Improving data quality using multiple, noisy labelers. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 614-622).
10. Smyth, P., Fayyad, U., Burl, M., Perona, P., & Baldi, P. (1994). Inferring ground truth from subjective labeling of Venus images. Advances in Neural Information Processing Systems 8 (pp. 1085-1092).
11. Teo, C. H., Globerson, A., Roweis, S. T., & Smola, A. (2007). Convex learning with invariances. Advances in Neural Information Processing Systems 21 (pp. 1489-1496).