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Poster
Bayesian n-Choose-k Models for Classification and Ranking
Kevin Swersky · Danny Tarlow · Richard Zemel · Ryan Adams · Brendan J Frey

Thu Dec 06 02:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor
in Poster Session »

In categorical data there is often structure in the number of variables that take on each label. For example, the total number of objects in an image and the number of highly relevant documents per query in web search both tend to follow a structured distribution. In this paper, we study a probabilistic model that explicitly includes a prior distribution over such counts, along with a count-conditional likelihood that defines probabilities over all subsets of a given size. When labels are binary and the prior over counts is a Poisson-Binomial distribution, a standard logistic regression model is recovered, but for other count distributions, such priors induce global dependencies and combinatorics that appear to complicate learning and inference. However, we demonstrate that simple, efficient learning procedures can be derived for more general forms of this model. We show the utility of the formulation by exploring multi-object classification as maximum likelihood learning, and ranking and top-K classification as loss-sensitive learning.

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Author Information

Kevin Swersky (Google)
Danny Tarlow (Google Research, Brain team)
Richard Zemel (Columbia University)
Ryan Adams (Princeton University)
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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