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8 - Computing Choice: Learning Distributions over Permutations

Published online by Cambridge University Press:  22 March 2021

By
Devavrat Shah
Edited by
Miguel R. D. Rodrigues  and
Yonina C. Eldar
Miguel R. D. Rodrigues
Affiliation:
University College London
Yonina C. Eldar
Affiliation:
Weizmann Institute of Science, Israel
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Summary

We discuss the question of learning distributions over permutations of a given set of choices, options or items based on partial observations. This is central to capturing the so-called “choice’’ in a variety of contexts. The question of learning distributions over permutations arises beyond capturing “choice’’ too, e.g., tracking a collection of objects using noisy cameras, or aggregating ranking of web-pages using outcomes of multiple search engines. Here we focus on learning distributions over permutations from marginal distributions of two types: first-order marginals and pair-wise comparisons. We emphasize the ability to identify the entire distribution over permutations as well as the “best ranking’’.

Keywords

learning from comparisionslearning from first-order marginalsrankingsparse modelrandom utility model
Type
Chapter
Information
Information-Theoretic Methods in Data Science , pp. 229 - 262
Publisher: Cambridge University Press
Print publication year: 2021

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