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In this paper, we derive non-asymptotic achievability and converse bounds on the random number generation with/without side-information. Our bounds are efficiently computable in the sense that the computational complexity does not depend on…

Information Theory · Computer Science 2016-09-28 Masahito Hayashi , Shun Watanabe

We consider a multi-hop distributed hypothesis testing problem with multiple decision centers (DCs) for testing against independence and where the observations obey some Markov chain. For this system, we characterize the fundamental type-II…

Information Theory · Computer Science 2022-08-31 Mustapha Hamad , Michèle Wigger , Mireille Sarkiss

We consider the multi-user lossy source-coding problem for continuous alphabet sources. In a previous work, Ziv proposed a single-user universal coding scheme which uses uniform quantization with dither, followed by a lossless source…

Information Theory · Computer Science 2014-11-14 Avraham Reani , Neri Merhav

Cut-set bounds on achievable rates for network communication protocols are not in general tight. In this paper we introduce a new technique for proving converses for the problem of transmission of correlated sources in networks, that…

Information Theory · Computer Science 2011-05-31 Amin Aminzadeh Gohari , Shenghao Yang , Sidharth Jaggi

This paper studies the augmented truncation of discrete-time block-monotone Markov chains under geometric drift conditions. We first present a bound for the total variation distance between the stationary distributions of an original Markov…

Probability · Mathematics 2014-07-18 Hiroyuki Masuyama

We provide upper bounds on the perturbation of invariant subspaces of normal matrices measured using a metric on the space of vector subspaces of $\mathbb{C}^n$ in terms of the spectrum of both the unperturbed \& perturbed matrices, as well…

Spectral Theory · Mathematics 2021-06-22 Subhrajit Bhattacharya

We improve the existing achievable rate regions for causal and for zero-delay source coding of stationary Gaussian sources under an average mean squared error (MSE) distortion measure. To begin with, we find a closed-form expression for the…

Information Theory · Computer Science 2011-05-03 Milan S. Derpich , Jan Østergaard

An outer bound to the two-receiver discrete memoryless broadcast channel is presented. We compare it to the known outer bounds and show that the outer bound presented is at least as tight as the existing bounds.

Information Theory · Computer Science 2008-07-24 Chandra Nair

A fundamental problem in decentralized networked systems is to coordinate actions of different agents so that they reach a state of agreement. In such applications, it is additionally desirable that the actions at various nodes may not be…

Information Theory · Computer Science 2024-11-22 Viswanathan Ramachandran , Tobias J. Oechtering , Mikael Skoglund

We revisit the Gray-Wyner lossy source coding problem and derive the first-order asymptotic optimal rate-distortion-perception region when additional perception constraints are imposed on reproduced source sequences. The optimal trade-off…

Information Theory · Computer Science 2026-01-19 Yu Yang , Yingxin Zhang , Weijie Yuan , Lin Zhou

We introduce a new technique to certify lower bounds on the multicut size using network coding. In directed networks the network coding rate is not a lower bound on the multicut, but we identify a class of networks on which the rate is…

Combinatorics · Mathematics 2013-05-17 Anna Blasiak

The sharpest known high probability generalization bounds for uniformly stable algorithms (Feldman, Vondr\'{a}k, 2018, 2019), (Bousquet, Klochkov, Zhivotovskiy, 2020) contain a generally inevitable sampling error term of order…

Machine Learning · Computer Science 2021-11-19 Yegor Klochkov , Nikita Zhivotovskiy

We present improved upper and lower bounds on the spanning ratio of $\theta$-graphs with at least six cones. Given a set of points in the plane, a $\theta$-graph partitions the plane around each vertex into $m$ disjoint cones, each having…

Computational Geometry · Computer Science 2014-04-25 Prosenjit Bose , Jean-Lou De Carufel , Pat Morin , André van Renssen , Sander Verdonschot

A multiple access channel and a point-to-point channel sharing the same medium for communications are considered. We obtain an outer bound for the capacity region of this setup, and we show that this outer bound is achievable in some cases.…

Information Theory · Computer Science 2010-11-01 Anas Chaaban , Aydin Sezgin

The Gap-Hamming-Distance problem arose in the context of proving space lower bounds for a number of key problems in the data stream model. In this problem, Alice and Bob have to decide whether the Hamming distance between their $n$-bit…

Computational Complexity · Computer Science 2009-02-17 Joshua Brody , Amit Chakrabarti

We establish a new extremal inequality, which is further leveraged to give a complete characterization of the rate region of the vector Gaussian CEO problem with the trace distortion constraint. The proof of this extremal inequality hinges…

Information Theory · Computer Science 2014-02-21 Yinfei Xu , Qiao Wang

We consider the minimum spanning tree (MST) problem under the restriction that for every vertex v, the edges of the tree that are adjacent to v satisfy a given family of constraints. A famous example thereof is the classical…

Data Structures and Algorithms · Computer Science 2011-07-28 Rico Zenklusen

This paper studies channel coding for the discrete memoryless multiple-access channel with a given (possibly suboptimal) decoding rule. A multi-letter successive decoding rule depending on an arbitrary non-negative decoding metric is…

Information Theory · Computer Science 2017-12-08 Jonathan Scarlett , Alfonso Martinez , Albert Guillén i Fàbregas

Consider the following information theoretic setup wherein independent codebooks of N correlated random variables are generated according to their respective marginals. The problem of determining the conditions on the rates of codebooks to…

Information Theory · Computer Science 2012-05-08 Kumar Viswanatha , Emrah Akyol , Kenneth Rose

Tensor train (TT) decomposition provides a space-efficient representation for higher-order tensors. Despite its advantage, we face two crucial limitations when we apply the TT decomposition to machine learning problems: the lack of…

Machine Learning · Statistics 2017-08-03 Masaaki Imaizumi , Takanori Maehara , Kohei Hayashi