Z. Cao, T. Qin, T. Liu, M. Tsai, and H. Li, Learning to rank, Proceedings of the 24th international conference on Machine learning, ICML '07, pp.129-136, 2007.
DOI : 10.1145/1273496.1273513

O. Chapelle and Y. Chang, Yahoo! learning to rank challenge overview, Yahoo Learning to Rank Challenge (JMLR W&CP), pp.1-24, 2010.

O. Chapelle, D. Metlzer, Y. Zhang, and P. Grinspan, Expected reciprocal rank for graded relevance, Proceeding of the 18th ACM conference on Information and knowledge management, CIKM '09, pp.621-630, 2009.
DOI : 10.1145/1645953.1646033
URL : http://ciir.cs.umass.edu/~metzler/metzler-cikm09.pdf

O. Chapelle and M. Wu, Gradient descent optimization of smoothed information retrieval metrics, Information Retrieval, vol.20, issue.4, pp.216-235, 2010.
DOI : 10.1007/s10791-009-9110-3

D. Cossock and T. Zhang, Statistical Analysis of Bayes Optimal Subset Ranking, IEEE Transactions on Information Theory, vol.54, issue.11, pp.5140-5154, 2008.
DOI : 10.1109/TIT.2008.929939

Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer, An efficient boosting algorithm for combining preferences, Journal of Machine Learning Research, vol.4, pp.933-969, 2003.

Y. Freund and R. E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, pp.119-139, 1997.
DOI : 10.1006/jcss.1997.1504

R. Herbrich, T. Graepel, and K. Obermayer, Large margin rank boundaries for ordinal regression, Advances in Large Margin Classifiers, pp.115-132, 2000.

B. Kégl and R. Busa-fekete, Boosting products of base classifiers, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.497-504, 2009.
DOI : 10.1145/1553374.1553439

P. Li, C. Burges, and Q. Wu, McRank: Learning to rank using multiple classification and gradient boosting, Advances in Neural Information Processing Systems, pp.897-904, 2007.

D. Mease and A. Wyner, Evidence contrary to the statistical view of boosting, Journal of Machine Learning Research, vol.9, pp.131-156, 2007.

A. Niculescu-mizil and R. Caruana, Obtaining calibrated probabilities from boosting, Proceedings of the 21st International Conference on Uncertainty in Artificial Intelligence, pp.413-420, 2005.

J. Rissanen, A Universal Prior for Integers and Estimation by Minimum Description Length, The Annals of Statistics, vol.11, issue.2, pp.416-431, 1983.
DOI : 10.1214/aos/1176346150

S. Robertson and H. Zaragoza, The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf, Retr, vol.3, pp.333-389, 2009.

R. E. Schapire and Y. Singer, Improved boosting algorithms using confidence-rated predictions, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.297-336, 1999.
DOI : 10.1145/279943.279960

H. Valizadegan, R. Jin, R. Zhang, and J. Mao, Learning to rank by optimizing NDCG measure, Advances in Neural Information Processing Systems 22, pp.1883-1891, 2009.

Q. Wu, C. J. Burges, K. M. Svore, and J. Gao, Adapting boosting for information retrieval measures, Information Retrieval, vol.10, issue.3, pp.254-270, 2010.
DOI : 10.1007/s10791-009-9112-1

J. Xu and H. Li, AdaRank, Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07, pp.391-398, 2007.
DOI : 10.1145/1277741.1277809