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Error-correcting codes are one of the most fundamental objects in pseudorandomness, with applications in communication, complexity theory, and beyond. Codes are useful because of their ability to support decoding, which is the task of…

Information Theory · Computer Science 2024-08-28 Shashank Srivastava

We give a new framework based on graph regularity lemmas, for list decoding and list recovery of codes based on spectral expanders. Using existing algorithms for computing regularity decompositions of sparse graphs in (randomized)…

Data Structures and Algorithms · Computer Science 2025-07-18 Shashank Srivastava , Madhur Tulsiani

We develop new list decoding algorithms for Tanner codes and distance-amplified codes based on bipartite spectral expanders. We show that proofs exhibiting lower bounds on the minimum distance of these codes can be used as certificates…

Data Structures and Algorithms · Computer Science 2023-11-07 Fernando Granha Jeronimo , Shashank Srivastava , Madhur Tulsiani

We introduce a novel family of expander-based error correcting codes. These codes can be sampled with randomness linear in the block-length, and achieve list-decoding capacity (among other local properties). Our expander-based codes can be…

Combinatorics · Mathematics 2023-04-11 Aaron L Putterman , Edward Pyne

We prove the following results concerning the list decoding of error-correcting codes: (i) We show that for \textit{any} code with a relative distance of $\delta$ (over a large enough alphabet), the following result holds for \textit{random…

Information Theory · Computer Science 2010-01-13 Atri Rudra , Steve Uurtamo

In this work, we introduce a framework to study the effect of random operations on the combinatorial list-decodability of a code. The operations we consider correspond to row and column operations on the matrix obtained from the code by…

Information Theory · Computer Science 2014-08-12 Atri Rudra , Mary Wootters

We develop a new approach for approximating large independent sets when the input graph is a one-sided spectral expander - that is, the uniform random walk matrix of the graph has its second eigenvalue bounded away from 1. Consequently, we…

Data Structures and Algorithms · Computer Science 2024-11-07 Mitali Bafna , Jun-Ting Hsieh , Pravesh K. Kothari

We study the classical expander codes, introduced by Sipser and Spielman \cite{SS96}. Given any constants $0< \alpha, \varepsilon < 1/2$, and an arbitrary bipartite graph with $N$ vertices on the left, $M < N$ vertices on the right, and…

Information Theory · Computer Science 2022-01-11 Xue Chen , Kuan Cheng , Xin Li , Minghui Ouyang

Expander graphs play a central role in graph theory and algorithms. With a number of powerful algorithmic tools developed around them, such as the Cut-Matching game, expander pruning, expander decomposition, and algorithms for decremental…

Data Structures and Algorithms · Computer Science 2022-12-12 Julia Chuzhoy

Multivariate multiplicity codes (Kopparty, Saraf, and Yekhanin, J. ACM 2014) are linear codes where the codewords are described by evaluations of multivariate polynomials (with a degree bound) and their derivatives up to a fixed order, on a…

Information Theory · Computer Science 2024-12-03 S. Venkitesh

We construct a new family of explicit codes that are list decodable to capacity and achieve an optimal list size of $O(\frac{1}{\epsilon})$. In contrast to existing explicit constructions of codes achieving list decoding capacity, our…

Information Theory · Computer Science 2025-02-12 Fernando Granha Jeronimo , Tushant Mittal , Shashank Srivastava , Madhur Tulsiani

We consider families of codes obtained by "lifting" a base code $\mathcal{C}$ through operations such as $k$-XOR applied to "local views" of codewords of $\mathcal{C}$, according to a suitable $k$-uniform hypergraph. The $k$-XOR operation…

Data Structures and Algorithms · Computer Science 2020-11-12 Vedat Levi Alev , Fernando Granha Jeronimo , Dylan Quintana , Shashank Srivastava , Madhur Tulsiani

This paper is concerned with list decoding of $2$-interleaved binary alternant codes. The principle of the proposed algorithm is based on a combination of a list decoding algorithm for (interleaved) Reed-Solomon codes and an algorithm for…

Information Theory · Computer Science 2022-02-14 Chih-Chiang Huang , Hedongliang Liu , Lukas Holzbaur , Sven Puchinger , Antonia Wachter-Zeh

Inspired by the recent advances in deep learning (DL), this work presents a deep neural network aided decoding algorithm for binary linear codes. Based on the concept of deep unfolding, we design a decoding network by unfolding the…

Information Theory · Computer Science 2020-02-19 Yi Wei , Ming-Min Zhao , Min-Jian Zhao , Ming Lei

We consider the problem of erasure/list decoding using certain classes of simplified decoders. Specifically, we assume a class of erasure/list decoders, such that a codeword is in the list if its likelihood is larger than a threshold. This…

Information Theory · Computer Science 2016-11-17 Nir Weinberger , Neri Merhav

We show that expander codes, when properly instantiated, are high-rate list recoverable codes with linear-time list recovery algorithms. List recoverable codes have been useful recently in constructing efficiently list-decodable codes, as…

Information Theory · Computer Science 2015-03-09 Brett Hemenway , Mary Wootters

A construction of expander codes is presented with the following three properties: (i) the codes lie close to the Singleton bound, (ii) they can be encoded in time complexity that is linear in their code length, and (iii) they have a…

Information Theory · Computer Science 2016-11-17 Ron M. Roth , Vitaly Skachek

We give a linear-time erasure list-decoding algorithm for expander codes. More precisely, let $r > 0$ be any integer. Given an inner code $C_0$ of length $d$, and a $d$-regular bipartite expander graph $G$ with $n$ vertices on each side, we…

Information Theory · Computer Science 2020-02-21 Noga Ron-Zewi , Mary Wootters , Gilles Zémor

This paper addresses the problem of learning binary hash codes for large scale image search by proposing a novel hashing method based on deep neural network. The advantage of our deep model over previous deep model used in hashing is that…

Computer Vision and Pattern Recognition · Computer Science 2015-08-31 Thanh-Toan Do , Anh-Zung Doan , Ngai-Man Cheung

We design the first efficient algorithms and prove new combinatorial bounds for list decoding tensor products of codes and interleaved codes. We show that for {\em every} code, the ratio of its list decoding radius to its minimum distance…

Information Theory · Computer Science 2008-11-27 Parikshit Gopalan , Venkatesan Guruswami , Prasad Raghavendra
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