Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

1 results

  • 8 - Computing Choice: Learning Distributions over Permutations

  • Edited by Miguel R. D. Rodrigues, University College London, Yonina C. Eldar, Weizmann Institute of Science, Israel
  • Book:
    Information-Theoretic Methods in Data Science
    Published online:
    22 March 2021
    Print publication:
    08 April 2021, pp 229-262

  • 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’’.