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Reconstructing lens potentials and lensed sources can easily become an underconstrained problem, even when the degrees of freedom are low, due to degeneracies, particularly when potential perturbations superimposed on a smooth lens are…

Astrophysics of Galaxies · Physics 2022-07-27 Georgios Vernardos , Leon V. E. Koopmans

While recent neural codecs achieve strong performance at low bitrates when optimized for perceptual quality, their effectiveness deteriorates significantly under ultra-low bitrate conditions. To mitigate this, generative compression methods…

Computer Vision and Pattern Recognition · Computer Science 2026-02-06 Chuqin Zhou , Xiaoyue Ling , Yunuo Chen , Jincheng Dai , Guo Lu , Wenjun Zhang

For a number of lossy source coding problems it is shown that even if the usual single-letter sum-rate-distortion expressions may become invalid for non-infinite distortion functions, they can be approached, to any desired accuracy, via the…

Information Theory · Computer Science 2014-08-05 Prakash Ishwar

Mixed-integer optimisation problems can be computationally challenging. Here, we introduce and analyse two efficient algorithms with a specific sequential design that are aimed at dealing with sampled problems within this class. At each…

Optimization and Control · Mathematics 2023-03-07 Mohammadreza Chamanbaz , Roland Bouffanais

The problem of secure source coding with multiple terminals is extended by considering a remote source whose noisy measurements are the correlated random variables used for secure source reconstruction. The main additions to the problem…

Information Theory · Computer Science 2022-11-10 Onur Günlü , Rafael F. Schaefer , Holger Boche , H. Vincent Poor

We determine the rate region of the Gaussian scalar-help-vector source-coding problem under a covariance matrix distortion constraint. The rate region is achieved by a Gaussian achievable scheme. We introduce a novel outer bounding…

Information Theory · Computer Science 2010-01-27 Md Saifur Rahman , Aaron B. Wagner

Recently, Stochastic Variational Inference (SVI) has been increasingly attractive thanks to its ability to find good posterior approximations of probabilistic models. It optimizes the variational objective with stochastic optimization,…

Machine Learning · Computer Science 2022-03-16 Minta Liu , Suliang Bu

In this paper we study the nonasymptotic bounds of a special Joint Source-Channel Coding system with hierarchical source, where an observable source and an unobservable indirect source are required to be reconstructed. Namely, we focus on…

Information Theory · Computer Science 2026-03-17 Shuo Shao , Chao Qi , Jincheng Dai

The three-node multiterminal lossy source coding problem is investigated. We derive an inner bound to the general rate-distortion region of this problem which is a natural extension of the seminal work by Kaspi'85 on the interactive…

Information Theory · Computer Science 2016-01-19 Leonardo Rey Vega , Pablo Piantanida , Alfred Hero

Convolutive source separation is often done in two stages: 1) estimation of the mixing filters and 2) estimation of the sources. Traditional approaches suffer from the ambiguities of arbitrary permutations and scaling in each frequency bin…

Combinatorics · Mathematics 2011-11-14 Alexis Benichoux , Prasad Sudhakar , Frédéric Bimbot , Rémi Gribonval

In super-resolution it is necessary to locate with high precision point sources from noisy observations of the spectrum of the signal at low frequencies capped by f_c. In the case when the point sources are positive and are located on a…

Information Theory · Computer Science 2020-05-15 Veniamin I. Morgenshtern

We study a new class of codes for Gaussian multi-terminal source and channel coding. These codes are designed using the statistical framework of high-dimensional linear regression and are called Sparse Superposition or Sparse Regression…

Information Theory · Computer Science 2012-12-11 Ramji Venkataramanan , Sekhar Tatikonda

We consider rate-distortion with two decoders, each with distinct side information. This problem is well understood when the side information at the decoders satisfies a certain degradedness condition. We consider cases in which this…

Information Theory · Computer Science 2016-12-13 Sinem Unal , Aaron B. Wagner

We consider finite blocklength lossy compression of information sources whose components are independent but non-identically distributed. Crucially, Gaussian sources with memory and quadratic distortion can be cast in this form. We show…

Information Theory · Computer Science 2026-02-11 Eyyup Tasci , Victoria Kostina

In this work we explore the fidelity of numerical approximations to the analytic spectra of hyperbolic partial differential equation systems with variable coefficients. We are particularly interested in the ability of discrete methods to…

Numerical Analysis · Mathematics 2025-08-12 Brittany A. Erickson

We consider the distributed source coding system for $L$ correlated Gaussian observations $Y_i, i=1,2, ..., L$. Let $X_i,i=1,2, ..., L$ be $L$ correlated Gaussian random variables and $N_i,$ $i=1,2,... L$ be independent additive Gaussian…

Information Theory · Computer Science 2009-08-09 Yasutada Oohama

We introduce deterministic perturbation schemes for the recently proposed random directions stochastic approximation (RDSA) [17], and propose new first-order and second-order algorithms. In the latter case, these are the first second-order…

Optimization and Control · Mathematics 2019-03-29 Prashanth L A , Shalabh Bhatnagar , Nirav Bhavsar , Michael Fu , Steven I. Marcus

We consider a Shannon cipher system for memoryless sources, in which distortion is allowed at the legitimate decoder. The source is compressed using a rate distortion code secured by a shared key, which satisfies a constraint on the…

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

We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements from an underlying random process, code and transmit those…

Information Theory · Computer Science 2007-07-13 Nan Liu , Sennur Ulukus

Learning distribution families over $\mathbb{R}^d$ is a fundamental problem in unsupervised learning and statistics. A central question in this setting is whether a given family of distributions possesses sufficient structure to be (at…

Machine Learning · Statistics 2025-06-10 Arefe Boushehrian , Amir Najafi
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