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We analyse the prequential plug-in codes relative to one-parameter exponential families M. We show that if data are sampled i.i.d. from some distribution outside M, then the redundancy of any plug-in prequential code grows at rate larger…

Information Theory · Computer Science 2010-02-04 Peter Grünwald , Wojciech Kotłowski

Binary AIFV codes are lossless codes that generalize the class of instantaneous FV codes. The code uses two code trees and assigns source symbols to incomplete internal nodes as well as to leaves. AIFV codes are empirically shown to attain…

Information Theory · Computer Science 2017-08-02 Weihua Hu , Hirosuke Yamamoto , Junya Honda

For variable-length coding with an almost-sure distortion constraint, Zhang et al. show that for discrete sources the redundancy is upper bounded by $\log n/n$ and lower bounded (in most cases) by $\log n/(2n)$, ignoring lower order terms.…

Information Theory · Computer Science 2026-01-21 Sharang M. Sriramu , Aaron B. Wagner

The penalty incurred by imposing a finite delay constraint in lossless source coding of a memoryless source is investigated. It is well known that for the so-called block-to-variable and variable-to-variable codes, the redundancy decays at…

Information Theory · Computer Science 2016-11-17 Ofer Shayevitz , Eado Meron , Meir Feder , Ram Zamir

It is known that for memoryless sources, the average and maximal redundancy of fixed-to-variable length codes, such as the Shannon and Huffman codes, exhibit two modes of behavior for long blocks. It either converges to a limit or it has an…

Information Theory · Computer Science 2012-08-07 Neri Merhav , Wojciech Szpankowski

We present new lower and upper bounds for the compression rate of binary prefix codes optimized over memoryless sources according to two related exponential codeword length objectives. The objectives explored here are exponential-average…

Information Theory · Computer Science 2011-05-03 Michael B. Baer

This paper presents new lower and upper bounds for the compression rate of binary prefix codes optimized over memoryless sources according to various nonlinear codeword length objectives. Like the most well-known redundancy bounds for…

Information Theory · Computer Science 2010-10-08 Michael B. Baer

We show the existence of variable-rate rate-distortion codes that meet the disortion constraint almost surely and are minimax, i.e., strongly, universal with respect to an unknown source distribution and a distortion measure that is…

Information Theory · Computer Science 2022-11-29 Adeel Mahmood , Aaron B. Wagner

We study universal compression of sequences generated by monotonic distributions. We show that for a monotonic distribution over an alphabet of size $k$, each probability parameter costs essentially $0.5 \log (n/k^3)$ bits, where $n$ is the…

Information Theory · Computer Science 2007-07-13 Gil I. Shamir

A binary code is said to be a disjunctive list-decoding $s_L$-code, $s\ge1$, $L\ge1$, (briefly, LD $s_L$-code) if the code is identified by the incidence matrix of a family of finite sets in which the union of any $s$ sets can cover not…

Information Theory · Computer Science 2014-07-10 A. G. Dyachkov , I. V. Vorobyev , N. A. Polyanskii , V. Yu. Shchukin

In this paper, we investigate the redundancy of universal coding schemes on smooth parametric sources in the finite-length regime. We derive an upper bound on the probability of the event that a sequence of length $n$, chosen using…

Information Theory · Computer Science 2011-10-27 Ahmad Beirami , Faramarz Fekri

The trapping redundancy of a linear code is the number of rows of a smallest parity-check matrix such that no submatrix forms an $(a,b)$-trapping set. This concept was first introduced in the context of low-density parity-check (LDPC) codes…

Information Theory · Computer Science 2016-11-15 Yu Tsunoda , Yuichiro Fujiwara

Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to autoregressive generation by enabling parallel token prediction. However, practical dLLM decoding still suffers from high inference latency, which limits…

Computation and Language · Computer Science 2026-04-22 Zhenbang Du , Kejing Xia , Xinrui Zhong , Yonggan Fu , Nicolai Oswald , Binfei Ji , Brucek Khailany , Pavlo Molchanov , Yingyan Lin

Log-linear models are typically fitted to contingency table data to describe and identify the relationship between different categorical variables. However, the data may include observed zero cell entries. The presence of zero cell entries…

Methodology · Statistics 2022-12-01 Serveh Sharifi Far , Michail Papathomas , Ruth King

Clarke and Barron have recently shown that the Jeffreys' invariant prior of Bayesian theory yields the common asymptotic (minimax and maximin) redundancy of universal data compression in a parametric setting. We seek a possible analogue of…

Probability · Mathematics 2016-11-17 Christian Krattenthaler , Paul B. Slater

Let A(q,n,d) denote the maximum size of a q-ary code of length n and distance d. We study the minimum asymptotic redundancy \rho(q,n,d)=n-log_q A(q,n,d) as n grows while q and d are fixed. For any d and q<=d-1, long algebraic codes are…

Information Theory · Computer Science 2007-07-13 Sergey Yekhanin , Ilya Dumer

Let C be a linear code with length n and minimum distance d. The stopping redundancy of C is defined as the minimum number of rows in a parity-check matrix for C such that the smallest stopping sets in the corresponding Tanner graph have…

Information Theory · Computer Science 2007-07-13 Junsheng Han , Paul H. Siegel

The efficiency of a code is estimated by its redundancy $R$, while the complexity of a code is estimated by its average delay $\bar N$. In this work we construct word-based codes, for which $R \lesssim \bar N^{-5/3}$. Therefore, word-based…

Information Theory · Computer Science 2007-12-04 G. L. Khodak

The minimum average number of bits need to describe a random variable is its entropy, assuming knowledge of the underlying statistics On the other hand, universal compression supposes that the distribution of the random variable, while…

Information Theory · Computer Science 2014-04-02 Maryam Hosseini , Narayana Santhanam

Memorization in large language models poses critical risks for privacy and fairness as these systems scale to billions of parameters. While previous studies established correlations between memorization and factors like token frequency and…

Machine Learning · Computer Science 2025-09-01 Jie Zhang , Qinghua Zhao , Chi-ho Lin , Zhongfeng Kang , Lei Li
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