IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
A Classification Framework for Anomaly Detection
The Journal of Machine Learning Research
Generalization Bounds for the Area Under the ROC Curve
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
The Journal of Machine Learning Research
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Ranking and scoring using empirical risk minimization
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
Regression Level Set Estimation Via Cost-Sensitive Classification
IEEE Transactions on Signal Processing
A Neyman-Pearson approach to statistical learning
IEEE Transactions on Information Theory
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Block-quantized support vector ordinal regression
IEEE Transactions on Neural Networks
IEEE Transactions on Information Theory
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
The Journal of Machine Learning Research
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
The Journal of Machine Learning Research
Batch and online learning algorithms for nonconvex neyman-pearson classification
ACM Transactions on Intelligent Systems and Technology (TIST)
On the ERA ranking representability of pairwise bipartite ranking functions
Artificial Intelligence
Learning Transformation Models for Ranking and Survival Analysis
The Journal of Machine Learning Research
Natural Language Processing (Almost) from Scratch
The Journal of Machine Learning Research
On-demand numerosity reduction for object learning
Proceedings of the workshop on Internet of Things and Service Platforms
Top-k learning to rank: labeling, ranking and evaluation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
A new probabilistic model for top-k ranking problem
Proceedings of the 21st ACM international conference on Information and knowledge management
The Journal of Machine Learning Research
A statistical view of clustering performance through the theory of U-processes
Journal of Multivariate Analysis
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We formulate a local form of the bipartite ranking problem where the goal is to focus on the best instances. We propose a methodology based on the construction of real-valued scoring functions. We study empirical risk minimization of dedicated statistics which involve empirical quantiles of the scores. We first state the problem of finding the best instances which can be cast as a classification problem with mass constraint. Next, we develop special performance measures for the local ranking problem which extend the Area Under an ROC Curve (AUC) criterion and describe the optimal elements of these new criteria. We also highlight the fact that the goal of ranking the best instances cannot be achieved in a stage-wise manner where first, the best instances would be tentatively identified and then a standard AUC criterion could be applied. Eventually, we state preliminary statistical results for the local ranking problem.