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Related papers: Minimax R\'enyi Redundancy

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The R\'enyi information measures are characterized in terms of their Shannon counterparts, and properties of the former are recovered from first principle via the associated properties of the latter. Motivated by this characterization, a…

Information Theory · Computer Science 2016-03-14 Ofer Shayevitz

An uncertainty relation for the R\'enyi entropies of conjugate quantum observables is used to obtain a strong Heisenberg limit of the form ${\rm RMSE} \geq f(\alpha)/(\langle N\rangle+\frac12)$, bounding the root mean square error of any…

Quantum Physics · Physics 2022-11-21 Michael J. W. Hall

The deletion channel is the simplest point-to-point communication channel that models lack of synchronization. Despite significant effort, little is known about its capacity, and even less about optimal coding schemes. In this paper we…

Information Theory · Computer Science 2011-05-02 Yashodhan Kanoria , Andrea Montanari

The random variable simulation problem consists in using a $k$-dimensional i.i.d. random vector $X^{k}$ with distribution $P_{X}^{k}$ to simulate an $n$-dimensional i.i.d. random vector $Y^{n}$ so that its distribution is approximately…

Information Theory · Computer Science 2018-12-24 Lei Yu , Vincent Y. F. Tan

This paper considers the performance of Reed-Muller (RM) codes transmitted over binary memoryless symmetric (BMS) channels under bitwise maximum-a-posteriori (bit-MAP) decoding. Its main result is that, for a fixed BMS channel, the family…

Information Theory · Computer Science 2023-06-14 Galen Reeves , Henry D. Pfister

Estimating divergences in a consistent way is of great importance in many machine learning tasks. Although this is a fundamental problem in nonparametric statistics, to the best of our knowledge there has been no finite sample exponential…

Information Theory · Computer Science 2016-03-30 Shashank Singh , Barnabás Póczos

Universally achievable error exponents pertaining to certain families of channels (most notably, discrete memoryless channels (DMC's)), and various ensembles of random codes, are studied by combining the competitive minimax approach,…

Information Theory · Computer Science 2007-08-01 Yaniv Akirav , Neri Merhav

We consider a novel variant of $d$-semifaithful lossy coding in which the distortion measure is revealed only to the encoder and only at run-time, as well as an extension of it in which the distortion constraint $d$ is also revealed at…

Information Theory · Computer Science 2022-10-05 Adeel Mahmood , Aaron B. Wagner

A classic result in algorithmic information theory is that every infinite binary sequence is computable from a Martin-Loef random infinite binary sequence. Proved independently by Kucera and Gacs, this result answered a question by Charles…

Computational Complexity · Computer Science 2017-06-13 George Barmpalias , Andrew Lewis-Pye

We show finite-size bounds on the deviation of the optimal type II error from its asymptotic value in the quantum hypothesis testing problem of Stein's lemma with composite null-hypothesis. The proof is based on some simple properties of a…

Quantum Physics · Physics 2014-07-07 Milán Mosonyi

Based on the problem of quantum data compression in a lossless way, we present here an operational interpretation for the family of quantum R\'enyi entropies. In order to do this, we appeal to a very general quantum encoding scheme that…

Quantum Physics · Physics 2017-10-05 G. Bellomo , G. M. Bosyk , F. Holik , S. Zozor

Properties of scalar quantization with $r$th power distortion and constrained R\'enyi entropy of order $\alpha\in (0,1)$ are investigated. For an asymptotically (high-rate) optimal sequence of quantizers, the contribution to the R\'enyi…

Information Theory · Computer Science 2012-03-27 Wolfgang Kreitmeier , Tamas Linder

We explicitly construct zero loss neural network classifiers. We write the weight matrices and bias vectors in terms of cumulative parameters, which determine truncation maps acting recursively on input space. The configurations for the…

Machine Learning · Computer Science 2026-01-14 Thomas Chen , Patrícia Muñoz Ewald

This paper investigates the information rate loss in analog channels when the sampler is designed to operate independent of the instantaneous channel occupancy. Specifically, a multiband linear time-invariant Gaussian channel under…

Information Theory · Computer Science 2017-03-14 Yuxin Chen , Andrea Goldsmith , Yonina C. Eldar

The partial information decomposition (PID) aims to quantify the amount of redundant information that a set of sources provides about a target. Here, we show that this goal can be formulated as a type of information bottleneck (IB) problem,…

Information Theory · Computer Science 2024-06-28 Artemy Kolchinsky

This paper presents a novel theoretical study of the general problem of multiple source adaptation using the notion of Renyi divergence. Our results build on our previous work [12], but significantly broaden the scope of that work in…

Machine Learning · Computer Science 2012-05-14 Yishay Mansour , Mehryar Mohri , Afshin Rostamizadeh

While the terms "redundancy" and "consensus" are often used as synonyms in the context of quantum objectivity, we show here that these should be understood as two related but distinct notions, that quantify different features of the…

Quantum Physics · Physics 2023-11-08 Diana A. Chisholm , Luca Innocenti , G. Massimo Palma

We obtain formulas for Petz-R\'enyi and Umegaki relative entropy from the idea of distribution of a positive selfadjoint operator. Classical results on R\'enyi and Kullback-Leibler divergences are applied to obtain new results and new…

Quantum Physics · Physics 2023-08-15 George Androulakis , Tiju Cherian John

Recently, the existence of considerable amount of redundancy in the Internet traffic has stimulated the deployment of several redundancy elimination techniques within the network. These techniques are often based on either packet-level…

Information Theory · Computer Science 2012-04-02 Mohsen Sardari , Ahmad Beirami , Faramarz Fekri

We formulate a new information-theoretic principle--the shifted composition rule--which bounds the divergence (e.g., Kullback-Leibler or R\'enyi) between the laws of two stochastic processes via the introduction of auxiliary shifts. In this…

Probability · Mathematics 2023-11-27 Jason M. Altschuler , Sinho Chewi
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