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We consider the stationaly memoryless channels with input cost. We prove that for transmission rates above the capacity the correct probability of decoding tends to zero exponentially as the block length $n$ of codes tends to infinity. In…

Information Theory · Computer Science 2025-09-26 Yasutada Oohama

Motivated by distributed storage applications, we investigate the degree to which capacity achieving encodings can be efficiently updated when a single information bit changes, and the degree to which such encodings can be efficiently…

Information Theory · Computer Science 2013-10-08 Arya Mazumdar , Venkat Chandar , Gregory W. Wornell

The AWGNC, BSC, and max-fractional pseudocodeword redundancies of a binary linear code are defined to be the smallest number of rows in a parity-check matrix such that the corresponding minimum pseudoweight is equal to the minimum Hamming…

Information Theory · Computer Science 2012-03-07 Jens Zumbrägel , Vitaly Skachek , Mark F. Flanagan

We consider the problem of distilling uniform random bits from an unknown source with a given $p$-entropy using linear hashing. As our main result, we estimate the expected $p$-divergence from the uniform distribution over the ensemble of…

Information Theory · Computer Science 2025-06-06 Madhura Pathegama , Alexander Barg

This paper presents lossless prefix codes optimized with respect to a pay-off criterion consisting of a convex combination of maximum codeword length and average codeword length. The optimal codeword lengths obtained are based on a new…

Information Theory · Computer Science 2012-08-18 Themistoklis Charalambous , Charalambos D. Charalambous , Farzad Rezaei

A lossy source code $\mathcal{C}$ with rate $R$ for a discrete memoryless source $S$ is called subset-universal if for every $0<R'< R$, almost every subset of $2^{nR'}$ of its codewords achieves average distortion close to the source's…

Information Theory · Computer Science 2015-03-13 Or Ordentlich , Ofer Shayevitz

In extreme classification problems, learning algorithms are required to map instances to labels from an extremely large label set. We build on a recent extreme classification framework with logarithmic time and space, and on a general…

Machine Learning · Computer Science 2018-12-13 Itay Evron , Edward Moroshko , Koby Crammer

We discuss inequalities holding between the vocabulary size, i.e., the number of distinct nonterminal symbols in a grammar-based compression for a string, and the excess length of the respective universal code, i.e., the code-based analog…

Information Theory · Computer Science 2020-03-11 Lukasz Debowski

We propose a simple, scalable, and fast gradient descent algorithm to optimize a nonconvex objective for the rank minimization problem and a closely related family of semidefinite programs. With $O(r^3 \kappa^2 n \log n)$ random…

Machine Learning · Statistics 2016-03-25 Qinqing Zheng , John Lafferty

We consider the one helper source coding problem posed and investigated by Ahlswede, K\"orner and Wyner. In this system, the error probability of decoding goes to one as the source block length $n$ goes to infinity. This implies that we…

Information Theory · Computer Science 2019-01-18 Yasutada Oohama

We characterize the resolvability region for a large class of point-to-point channels with continuous alphabets. In our direct result, we prove not only the existence of good resolvability codebooks, but adapt an approach based on the…

Information Theory · Computer Science 2019-07-03 Matthias Frey , Igor Bjelaković , Sławomir Stańczak

A quantum system interacts with its environment, if ever so slightly, no matter how much care is put into isolating it. As a consequence, quantum bits (qubits) undergo errors, putting dauntingly difficult constraints on the hardware…

Slow running or straggler tasks can significantly reduce computation speed in distributed computation. Recently, coding-theory-inspired approaches have been applied to mitigate the effect of straggling, through embedding redundancy in…

Machine Learning · Statistics 2018-01-24 Can Karakus , Yifan Sun , Suhas Diggavi , Wotao Yin

This paper considers guessing-based decoders with abandonment for discrete memoryless channels in which all codewords have the same composition. This class of decoders rank-orders all input sequences in the codebook's composition class from…

Information Theory · Computer Science 2025-08-11 Vincent Y. F. Tan , Hamdi Joudeh

We present a new lossy compressor for discrete sources. For coding a source sequence $x^n$, the encoder starts by assigning a certain cost to each reconstruction sequence. It then finds the reconstruction that minimizes this cost and…

Information Theory · Computer Science 2009-01-19 Shirin Jalali , Andrea Montanari , Tsachy Weissman

This paper shows that the probability that the error exponent of a given code randomly generated from a pairwise independent ensemble being smaller than a lower bound on the typical random-coding exponent tends to zero as the codeword…

Information Theory · Computer Science 2022-04-04 Giuseppe Cocco , Albert Guillén i Fàbregas , Josep Font-Segura

This paper studies on the cardinality of perfect multi deletion binary codes. The lower bound for any perfect deletion code with the fixed code length and the number of deletions, and the asymptotic achievable of Levenshtein's upper bound…

Combinatorics · Mathematics 2019-10-16 Takehiko Mori , Manabu Hagiwara

Function-correcting codes (FCCs) protect specific function evaluations of a message against errors. This condition imposes a less stringent distance requirement than classical error-correcting codes (ECCs), allowing for reduced redundancy.…

Information Theory · Computer Science 2025-07-15 Hoang Ly , Emina Soljanin

Numerical multiscale methods usually rely on some coupling between a macroscopic and a microscopic model. The macroscopic model is incomplete as effective quantities, such as the homogenized material coefficients or fluxes, are missing in…

Numerical Analysis · Mathematics 2021-03-23 Assyr Abdulle , Doghonay Arjmand , Edoardo Paganoni

Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition. We study the related issue of receiving…

Machine Learning · Computer Science 2020-10-06 Sean Welleck , Ilia Kulikov , Jaedeok Kim , Richard Yuanzhe Pang , Kyunghyun Cho