Convex calibrated surrogates for low-rank loss matrices with applications to subset ranking losses

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Ramaswamy, Harish G ^a   Agarwal, Shivani ^a   Tewari, Ambuj ^b  

^a INDIAN INSTITUTE OF SCIENCE   (India)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EXPLICIT CONSTRUCTIONS; LEAST SQUARE; MULTI-CLASS LEARNING; RANKING PROBLEMS; TARGET SPACE;

EID: 84899003560     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Gábor Lugosi and Nicolas Vayatis. On the Bayes-risk consistency of regularized boosting methods. Annals of Statistics, 32(1):30-55, 2004.
2. Wenxin Jiang. Process consistency for AdaBoost. Annals of Statistics, 32(1):13-29, 2004.
3. Tong Zhang. Statistical behavior and consistency of classification methods based on convex risk minimization. Annals of Statistics, 32(1):56-134, 2004.
4. Ingo Steinwart. Consistency of support vector machines and other regularized kernel classifiers. IEEE Transactions on Information Theory, 51(1):128-142, 2005.
5. Peter L. Bartlett, Michael Jordan, and Jon McAuliffe. Convexity, classification and risk bounds. Journal of the American Statistical Association, 101(473):138-156, 2006.
6. Tong Zhang. Statistical analysis of some multi-category large margin classification methods. Journal of Machine Learning Research, 5:1225-1251, 2004.
7. Ambuj Tewari and Peter L. Bartlett. On the consistency of multiclass classification methods. Journal of Machine Learning Research, 8:1007-1025, 2007.
8. Ingo Steinwart. How to compare different loss functions and their risks. Constructive Approximation, 26:225-287, 2007.
9. David Cossock and Tong Zhang. Statistical analysis of bayes optimal subset ranking. IEEE Transactions on Information Theory, 54(11):5140-5154, 2008.
10. Fen Xia, Tie-Yan Liu, JueWang, Wensheng Zhang, and Hang Li. Listwise approach to learning to rank: Theory and algorithm. In International Conference on Machine Learning, 2008.
11. John Duchi, Lester Mackey, and Michael Jordan. On the consistency of ranking algorithms. In International Conference on Machine Learning, 2010.
12. Pradeep Ravikumar, Ambuj Tewari, and Eunho Yang. On NDCG consistency of listwise ranking methods. In International Conference on Artificial Intelligence and Statistics, 2011.
13. David Buffoni, Clément Calauzènes, Patrick Gallinari, and Nicolas Usunier. Learning scoring functions with order-preserving losses and standardized supervision. In International Conference on Machine Learning, 2011.
14. Wei Gao and Zhi-Hua Zhou. On the consistency of multi-label learning. In Conference on Learning Theory, 2011.
15. Clément Calauzènes, Nicolas Usunier, and Patrick Gallinari. On the (non-)existence of convex, calibrated surrogate losses for ranking. In Advances in Neural Information Processing Systems 25, pages 197-205. 2012.
16. Harish G. Ramaswamy and Shivani Agarwal. Classification calibration dimension for general multiclass losses. In Advances in Neural Information Processing Systems 25, pages 2087- 2095. 2012.
17. Yanyan Lan, Jiafeng Guo, Xueqi Cheng, and Tie-Yan Liu. Statistical consistency of ranking methods in a rank-differentiable probability space. In Advances in Neural Information Processing Systems 25, pages 1241-1249. 2012.
18. Quoc V. Le and Alex Smola. Direct optimization of ranking measures, arXiv:0704.3359, 2007.
19. Yisong Yue, Thomas Finley, Filip Radlinski, and Thorsten Joachims. A support vector method for optimizing average precision. In Proceedings of the 30th ACM SIGIR International Conference on Research and Development in Information Retrieval, 2007.