MEMR: A Margin Equipped Monotone Retargeting Framework for Ranking

Sreangsu Acharyya, Joydeep Ghosh
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:853-862, 2014.

Abstract

We bring to bear the tools of convexity, mar- gins and the newly proposed technique of monotone retargeting upon the task of learn- ing permutations from examples. This leads to novel and efficient algorithms with guaran- teed prediction performance in the online set- ting and on global optimality and the rate of convergence in the batch setting. Monotone retargeting efficiently optimizes over all pos- sible monotone transformations as well as the finite dimensional parameters of the model. As a result we obtain an effective algorithm to learn transitive relationships over items. It captures the inherent combinatorial char- acteristics of the output space yet it has a computational burden not much more than that of a generalized linear model.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-acharyya14a, title = {{MEMR}: A Margin Equipped Monotone Retargeting Framework for Ranking}, author = {Acharyya, Sreangsu and Ghosh, Joydeep}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {853--862}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/acharyya14a/acharyya14a.pdf}, url = {https://proceedings.mlr.press/r12/acharyya14a.html}, abstract = {We bring to bear the tools of convexity, mar- gins and the newly proposed technique of monotone retargeting upon the task of learn- ing permutations from examples. This leads to novel and efficient algorithms with guaran- teed prediction performance in the online set- ting and on global optimality and the rate of convergence in the batch setting. Monotone retargeting efficiently optimizes over all pos- sible monotone transformations as well as the finite dimensional parameters of the model. As a result we obtain an effective algorithm to learn transitive relationships over items. It captures the inherent combinatorial char- acteristics of the output space yet it has a computational burden not much more than that of a generalized linear model.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T MEMR: A Margin Equipped Monotone Retargeting Framework for Ranking %A Sreangsu Acharyya %A Joydeep Ghosh %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-acharyya14a %I PMLR %P 853--862 %U https://proceedings.mlr.press/r12/acharyya14a.html %V R12 %X We bring to bear the tools of convexity, mar- gins and the newly proposed technique of monotone retargeting upon the task of learn- ing permutations from examples. This leads to novel and efficient algorithms with guaran- teed prediction performance in the online set- ting and on global optimality and the rate of convergence in the batch setting. Monotone retargeting efficiently optimizes over all pos- sible monotone transformations as well as the finite dimensional parameters of the model. As a result we obtain an effective algorithm to learn transitive relationships over items. It captures the inherent combinatorial char- acteristics of the output space yet it has a computational burden not much more than that of a generalized linear model. %Z Reissued by PMLR on 04 October 2026.
APA
Acharyya, S. & Ghosh, J.. (2014). MEMR: A Margin Equipped Monotone Retargeting Framework for Ranking. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:853-862 Available from https://proceedings.mlr.press/r12/acharyya14a.html. Reissued by PMLR on 04 October 2026.

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