An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Ordering by weighted number of wins gives a good ranking for weighted tournaments
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
SIAM Journal on Discrete Mathematics
Journal of Artificial Intelligence Research
Ranking and scoring using empirical risk minimization
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Stability and generalization of bipartite ranking algorithms
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Margin-Based ranking meets boosting in the middle
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Robust sparse rank learning for non-smooth ranking measures
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using 1-norm regularization and convex hull reduction
Proceedings of the 48th Annual Southeast Regional Conference
Ranking from pairs and triplets: information quality, evaluation methods and query complexity
Proceedings of the fourth ACM international conference on Web search and data mining
On Equivalence Relationships Between Classification and Ranking Algorithms
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Modeling topic dependencies in hierarchical text categorization
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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We reduce ranking, as measured by the Area Under the Receiver Operating Characteristic Curve (AUC), to binary classification. The core theorem shows that a binary classification regret of r on the induced binary problem implies an AUC regret of at most 2r. This is a large improvement over approaches such as ordering according to regressed scores, which have a regret transform of r 驴 nr where n is the number of elements.