@inproceedings{c692f48f9b634cfe85489a84f76f5574,
title = "Local list recovery of high-rate tensor codes & applications",
abstract = "In this work, we give the first construction of high-rate locally list-recoverable codes. List-recovery has been an extremely useful building block in coding theory, and our motivation is to use these codes as such a building block. In particular, our construction gives the first capacity-achieving locally list-decodable codes (over constant-sized alphabet); the first capacity achieving} globally list-decodable codes with nearly linear time list decoding algorithm (once more, over constant-sized alphabet); and a randomized construction of binary codes on the Gilbert-Varshamov bound that can be uniquely decoded in near-linear-time, with higher rate than was previously known.Our techniques are actually quite simple, and are inspired by an approach of Gopalan, Guruswami, and Raghavendra (Siam Journal on Computing, 2011) for list-decoding tensor codes. We show that tensor powers of (globally) list-recoverable codes are approximately locally list-recoverable, and that the approximately modifier may be removed by pre-encoding the message with a suitable locally decodable code. Instantiating this with known constructions of high-rate globally list-recoverable codes and high-rate locally decodable codes finishes the construction.",
keywords = "coding theory, error correcting codes, list recovery, local list recovery, tensor codes",
author = "Brett Hemenway and Noga Ron-Zewi and Mary Wootters",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 58th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2017 ; Conference date: 15-10-2017 Through 17-10-2017",
year = "2017",
month = nov,
day = "10",
doi = "10.1109/FOCS.2017.27",
language = "American English",
series = "Annual Symposium on Foundations of Computer Science - Proceedings",
publisher = "IEEE Computer Society",
pages = "204--215",
booktitle = "Proceedings - 58th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2017",
address = "United States",
}