MEMR: A Margin Equipped Monotone Retargeting Framework for Ranking
Sreangsu Acharyya, Joydeep Ghosh
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.
PDF Link: /papers/14/p2-acharyya.pdf
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"