@inproceedings{606f95fc7952455f928d5fc2663e1c1a,
title = "Upper bounds on the relative entropy and R{\'e}nyi divergence as a function of total variation distance for finite alphabets",
abstract = "A new upper bound on the relative entropy is derived as a function of the total variation distance for probability measures defined on a common finite alphabet. The bound improves a previously reported bound by Csisz{\'a}r and Talata. It is further extended to an upper bound on the R{\'e}nyi divergence of an arbitrary non-negative order (including ∞) as a function of the total variation distance.",
keywords = "Pinsker's inequality, R{\'e}nyi divergence, relative entropy, relative information, total variation distance",
author = "Igal Sason and Sergio Verdu",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Information Theory Workshop, ITW 2015 ; Conference date: 11-10-2015 Through 15-10-2015",
year = "2015",
month = dec,
day = "17",
doi = "10.1109/ITWF.2015.7360766",
language = "English (US)",
series = "ITW 2015 - 2015 IEEE Information Theory Workshop",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "214--218",
booktitle = "ITW 2015 - 2015 IEEE Information Theory Workshop",
address = "United States",
}