Corpus ID: 218763208

Learning to rank via combining representations

@article{Helm2020LearningTR,
  title={Learning to rank via combining representations},
  author={Hayden S. Helm and A. Basu and Avanti Athreya and Youngser Park and J. Vogelstein and Michael Winding and Marta Zlatic and Albert Cardona and P. Bourke and Jonathan Larson and Chris White and C. Priebe},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.10700}
}
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest. The setting we consider is one in which no analytic description of what constitutes a good ranking is available. Instead, we have a collection of representations and supervisory information consisting of a (target item, interesting items set) pair. We demonstrate -- analytically, in simulation, and in real data examples -- that learning to… Expand

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