Introduction to algorithms
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
A new family of online algorithms for category ranking
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Convex Optimization
Neural Computation
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
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
A formal analysis of stopping criteria of decomposition methods for support vector machines
IEEE Transactions on Neural Networks
The Interplay of Optimization and Machine Learning Research
The Journal of Machine Learning Research
Efficient projections onto the l1-ball for learning in high dimensions
Proceedings of the 25th international conference on Machine learning
Label ranking by learning pairwise preferences
Artificial Intelligence
Multilabel classification via calibrated label ranking
Machine Learning
Online Learning of Complex Prediction Problems Using Simultaneous Projections
The Journal of Machine Learning Research
Efficient Euclidean projections in linear time
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Distance metric learning from uncertain side information with application to automated photo tagging
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Ranking structured documents: a large margin based approach for patent prior art search
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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
Distance metric learning from uncertain side information for automated photo tagging
ACM Transactions on Intelligent Systems and Technology (TIST)
Efficient rank aggregation using partial data
Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems
Semi-supervised multi-label classification: a simultaneous large-margin, subspace learning approach
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Lead-lag analysis via sparse co-projection in correlated text streams
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Probabilistic multi-label classification with sparse feature learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Machine Learning
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We discuss the problem of learning to rank labels from a real valued feedback associated with each label. We cast the feedback as a preferences graph where the nodes of the graph are the labels and edges express preferences over labels. We tackle the learning problem by defining a loss function for comparing a predicted graph with a feedback graph. This loss is materialized by decomposing the feedback graph into bipartite sub-graphs. We then adopt the maximum-margin framework which leads to a quadratic optimization problem with linear constraints. While the size of the problem grows quadratically with the number of the nodes in the feedback graph, we derive a problem of a significantly smaller size and prove that it attains the same minimum. We then describe an efficient algorithm, called SOPOPO, for solving the reduced problem by employing a soft projection onto the polyhedron defined by a reduced set of constraints. We also describe and analyze a wrapper procedure for batch learning when multiple graphs are provided for training. We conclude with a set of experiments which show significant improvements in run time over a state of the art interior-point algorithm.