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4. Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to
listwise approach. In: Proceedings of the 24th International Conference on Machine Learning
(ICML 2007), pp. 129-136 (2007)
5. Carvalho, V.R., Elsas, J.L., Cohen, W.W., Carbonell, J.G.: A meta-learning approach for ro-
bust rank learning. In: SIGIR 2008 Workshop on Learning to Rank for Information Retrieval
(LR4IR 2008) (2008)
6. Chakrabarti, S., Khanna, R., Sawant, U., Bhattacharyya, C.: Structured learning for non-
smooth ranking losses. In: Proceedings of the 14th ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining (KDD 2008), pp. 88-96 (2008)
7. Chapelle, O., Wu, M.: Gradient descent optimization of smoothed information retrieval met-
rics. Information Retrieval Journal. Special Issue on Learning to Rank 13 (3), doi: 10.1007/
s10791-009-9110-3 (2010)
8. Cohen, W.W., Schapire, R.E., Singer, Y.: Learning to order things. In: Advances in Neural
Information Processing Systems 10 (NIPS 1997), vol. 10, pp. 243-270 (1998)
9. Cortes, C., Mohri, M., et al.: Magnitude-preserving ranking algorithms. In: Proceedings of the
24th International Conference on Machine Learning (ICML 2007), pp. 169-176 (2007)
10. Cossock, D., Zhang, T.: Subset ranking using regression. In: Proceedings of the 19th Annual
Conference on Learning Theory (COLT 2006), pp. 605-619 (2006)
11. Crammer, K., Singer, Y.: Pranking with ranking. In: Advances in Neural Information Process-
ing Systems 14 (NIPS 2001), pp. 641-647 (2002)
12. Freund, Y., Iyer, R., Schapire, R., Singer, Y.: An efficient boosting algorithm for combining
preferences. Journal of Machine Learning Research 4 , 933-969 (2003)
13. Fuhr, N.: Optimum polynomial retrieval functions based on the probability ranking principle.
ACM Transactions on Information Systems 7 (3), 183-204 (1989)
14. Gey, F.C.: Inferring probability of relevance using the method of logistic regression. In: Pro-
ceedings of the 17th Annual International ACM SIGIR Conference on Research and Devel-
opment in Information Retrieval (SIGIR 1994), pp. 222-231 (1994)
15. Herbrich, R., Obermayer, K., Graepel, T.: Large margin rank boundaries for ordinal regres-
sion. In: Advances in Large Margin Classifiers, pp. 115-132 (2000)
16. Huang, J., Frey, B.: Structured ranking learning using cumulative distribution networks. In:
Advances in Neural Information Processing Systems 21 (NIPS 2008) (2009)
17. Li, P., Burges, C., Wu, Q.: McRank: learning to rank using multiple classification and gradient
boosting. In: Advances in Neural Information Processing Systems 20 (NIPS 2007), pp. 845-
852 (2008)
18. Nallapati, R.: Discriminative models for information retrieval. In: Proceedings of the 27th
Annual International ACM SIGIR Conference on Research and Development in Information
Retrieval (SIGIR 2004), pp. 64-71 (2004)
19. Qin, T., Liu, T.Y., Lai, W., Zhang, X.D., Wang, D.S., Li, H.: Ranking with multiple hyper-
planes. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research
and Development in Information Retrieval (SIGIR 2007), pp. 279-286 (2007)
20. Qin, T., Liu, T.Y., Li, H.: A general approximation framework for direct optimization of infor-
mation retrieval measures. Information Retrieval 13 (4), 375-397 (2009)
21. Qin, T., Zhang, X.D., Tsai, M.F., Wang, D.S., Liu, T.Y., Li, H.: Query-level loss functions for
information retrieval. Information Processing and Management 44 (2), 838-855 (2008)
22. Rennie, J.D.M., Srebro, N.: Loss functions for preference levels: regression with discrete or-
dered labels. In: IJCAI 2005 Multidisciplinary Workshop on Advances in Preference Han-
dling. ACM, New York (2005)
23. Rigutini, L., Papini, T., Maggini, M., Scarselli, F.: Learning to rank by a neural-based sorting
algorithm. In: SIGIR 2008 Workshop on Learning to Rank for Information Retrieval (LR4IR
2008) (2008)
24. Rudin, C.: Ranking with a p-norm push. In: Proceedings of the 19th Annual Conference on
Learning Theory (COLT 2006), pp. 589-604 (2006)
25. Shashua, A., Levin, A.: Ranking with large margin principles: two approaches. In: Advances
in Neural Information Processing Systems 15 (NIPS 2002), pp. 937-944 (2003)
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