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, margins and the newly proposed technique of monotone retargeting upon the task of learning permutations from examples. This leads to novel and efficient algorithms with guaranteed prediction performance in the online setting and on global optimality and the rate of convergence in the batch setting. Monotone retargeting efficiently optimizes over all possible 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 characteristics 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, margins and the newly proposed technique of monotone retargeting upon the task of learning permutations from examples. This leads to novel and efficient algorithms with guaranteed prediction performance in the online setting and on global optimality and the rate of convergence in the batch setting. Monotone retargeting efficiently optimizes over all possible 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 characteristics 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, margins and the newly proposed technique of monotone retargeting upon the task of learning permutations from examples. This leads to novel and efficient algorithms with guaranteed prediction performance in the online setting and on global optimality and the rate of convergence in the batch setting. Monotone retargeting efficiently optimizes over all possible 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 characteristics 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.

Related Material