Uncertainty in Artificial Intelligence
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MEMR: A Margin Equipped Monotone Retargeting Framework for Ranking
Sreangsu Acharyya, Joydeep Ghosh
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.
Keywords:
Pages: 2-11
PS Link:
PDF Link: /papers/14/p2-acharyya.pdf
BibTex:
@INPROCEEDINGS{Acharyya14,
AUTHOR = "Sreangsu Acharyya and Joydeep Ghosh",
TITLE = "MEMR: A Margin Equipped Monotone Retargeting Framework for Ranking",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "2--11"
}


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