2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | S.9.4 | ||
| Paper Title | Approximate Gács-Körner Common Information | ||
| Authors | Salman Salamatian, MIT, United States; Asaf Cohen, Ben Gurion University of the Negev, Israel; Muriel Médard, MIT, United States | ||
| Session | S.9: Information Measures I | ||
| Presentation | Lecture | ||
| Track | Shannon Theory | ||
| Manuscript | Click here to download the manuscript | ||
| Virtual Presentation | Click here to watch in the Virtual Symposium | ||
| 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 P_X;Y . We consider embeddings of X into discrete random variables U, such that H(U|Y )<∂, while maximizing I(X;U). When ∂= 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. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
