Approximate Gács-Körner Common Information

Salman Salamatian, Asaf Cohen, Muriel Medard

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    3 Scopus citations

    Abstract

    We propose to exploit the structure of the correlation between two random variables X and Y via a relaxation on the Common Information problem of Gács and Körner (GK Common Information). Consider two correlated sources X and Y generated from a joint distribution PX,Y. We study embeddings of X into discrete random variables U, such that H(U|Y) ≤ δ, while maximizing I(X; U). When δ = 0, this reduces to the GK Common Information problem. However, unlike the GK Common Information, which is known to be zero for many pairs of random variables (X, Y), we show that this relaxation allows to capture the structure in the correlation between X and Y for a much broader range of joint distributions, and showcase applications for some problems in multi-terminal information theory.

    Original languageEnglish
    Title of host publication2020 IEEE International Symposium on Information Theory, ISIT 2020 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers
    Pages2234-2239
    Number of pages6
    ISBN (Electronic)9781728164328
    DOIs
    StatePublished - 1 Jun 2020
    Event2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, United States
    Duration: 21 Jul 202026 Jul 2020

    Publication series

    NameIEEE International Symposium on Information Theory - Proceedings
    Volume2020-June
    ISSN (Print)2157-8095

    Conference

    Conference2020 IEEE International Symposium on Information Theory, ISIT 2020
    Country/TerritoryUnited States
    CityLos Angeles
    Period21/07/2026/07/20

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Information Systems
    • Modeling and Simulation
    • Applied Mathematics

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