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Structured ranking learning using cumulative distribution networks
Jim C Huang · Brendan J Frey

Mon Dec 08 08:45 PM -- 12:00 AM (PST) @

Ranking is at the heart of many information retrieval applications. Unlike standard regression or classification, in which we predict outputs independently, in ranking, we are interested in predicting structured outputs so that misranking one object can significantly affect whether we correctly rank the other objects. In practice, the problem of ranking involves a large number of objects to be ranked and either approximate structured prediction methods are required, or assumptions of independence between object scores must be made in order to make the problem tractable. We present a probabilistic method for learning to rank using the graphical modelling framework of cumulative distribution networks (CDNs), where we can take into account the structure inherent to the problem of ranking by modelling the joint cumulative distribution functions (CDFs) over multiple pairwise preferences. We apply our framework to the problem of document retrieval in the case of the OHSUMED benchmark dataset. We will show that the RankNet, ListNet and ListMLE probabilistic models can be viewed as particular instances of CDNs and that our proposed framework allows for the exploration of a broad class of flexible structured loss functionals for ranking learning.

Author Information

Jim C Huang (Microsoft Research)
Brendan J Frey (Deep Genomics, Vector Institute, Univ. Toronto)

Brendan Frey is Co-Founder and CEO of Deep Genomics, a Co-Founder of the Vector Institute for Artificial Intelligence, and a Professor of Engineering and Medicine at the University of Toronto. He is internationally recognized as a leader in machine learning and in genome biology and his group has published over a dozen papers on these topics in Science, Nature and Cell. His work on using deep learning to identify protein-DNA interactions was recently highlighted on the front cover Nature Biotechnology (2015), while his work on deep learning dates back to an early paper on what are now called variational autoencoders (Science 1995). He is a Fellow of the Royal Society of Canada, a Fellow of the Institute for Electrical and Electronic Engineers, and a Fellow of the American Association for the Advancement of Science. He has consulted for several industrial research and development laboratories in Canada, the United States and England, and has served on the Technical Advisory Board of Microsoft Research.

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