The Journal of Machine Learning Research
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
Generalization Bounds for the Area Under the ROC Curve
The Journal of Machine Learning Research
A study of the bipartite ranking problem in machine learning
A study of the bipartite ranking problem in machine learning
Journal of Artificial Intelligence Research
Almost-everywhere algorithmic stability and generalization error
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Learnability of bipartite ranking functions
COLT'05 Proceedings of the 18th annual conference on Learning Theory
ICML '06 Proceedings of the 23rd international conference on Machine learning
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
The Journal of Machine Learning Research
Learning random walks to rank nodes in graphs
Proceedings of the 24th international conference on Machine learning
Magnitude-preserving ranking algorithms
Proceedings of the 24th international conference on Machine learning
Robust reductions from ranking to classification
Machine Learning
Query-level stability and generalization in learning to rank
Proceedings of the 25th international conference on Machine learning
Query dependent ranking using K-nearest neighbor
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Hinge Rank Loss and the Area Under the ROC Curve
ECML '07 Proceedings of the 18th European conference on Machine Learning
Generalization Bounds for Some Ordinal Regression Algorithms
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
An efficient algorithm for learning to rank from preference graphs
Machine Learning
Generalization analysis of listwise learning-to-rank algorithms
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
An alternative ranking problem for search engines
WEA'07 Proceedings of the 6th international conference on Experimental algorithms
Robust reductions from ranking to classification
COLT'07 Proceedings of the 20th annual conference on Learning theory
Learning a Robust Relevance Model for Search Using Kernel Methods
The Journal of Machine Learning Research
Effect on generalization of using relational information in list-wise algorithms
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Learning bilinear model for matching queries and documents
The Journal of Machine Learning Research
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The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in machine learning. We study generalization properties of ranking algorithms, in a particular setting of the ranking problem known as the bipartite ranking problem, using the notion of algorithmic stability.In particular, we derive generalization bounds for bipartite ranking algorithms that have good stability properties. We show that kernel-based ranking algorithms that perform regularization in a reproducing kernel Hilbert space have such stability properties, and therefore our bounds can be applied to these algorithms; this is in contrast with previous generalization bounds for ranking, which are based on uniform convergence and in many cases cannot be applied to these algorithms. A comparison of the bounds we obtain with corresponding bounds for classification algorithms yields some interesting insights into the difference in generalization behaviour between ranking and classification.