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JMLR
2012

Perturbation based Large Margin Approach for Ranking

6 years 6 months ago
Perturbation based Large Margin Approach for Ranking
We consider the task of devising large-margin based surrogate losses for the learning to rank problem. In this learning to rank setting, the traditional hinge loss for structured outputs faces two main challenges: (a) the supervision consists of instances with multiple training labels instead of a single label per instance, and (b) the label space of the set of all permutations of items is very large, and less amenable to the usual dynamic programming based methods. The most natural way to deal with multiple labels leads, unfortunately, to a non-convex surrogate. We address this by first providing a general class of convex perturbation based surrogates as an extension of the large margin method. Our experiments demonstrate that a simple surrogate from this class performs better than other candidate large margin proposals for the learning to rank task.
Eunho Yang, Ambuj Tewari, Pradeep D. Ravikumar
Added 27 Sep 2012
Updated 27 Sep 2012
Type Journal
Year 2012
Where JMLR
Authors Eunho Yang, Ambuj Tewari, Pradeep D. Ravikumar
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