About the Information Theory Society
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Information theory is the study of the mathematical fundamentals of information transmission and storage.
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Robert Calderbank - USC Viterbi Lecture, Thursday March 2, 2023
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Postdoctoral Researcher Position on Information Theory and Codings
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IEEE BITS the Information Theory Magazine
IEEE BITS the Information Theory Magazine publishes content that includes tutorials and review articles, historical surveys, and columns. The tutorial and review articles cover both traditional and emerging areas associated with Information Theory research and are written in a style accessible to readers outside the specialty of the article. The historical surveys are intended to highlight technological advances of current interest that have been significantly impacted by past Information Theory research.
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Recent Journal Issues
IEEE Journal on Selected Areas in Information Theory
IEEE Journal on Selected Areas in Information Theory
IEEE Transactions on Information Theory
The IEEE Transactions on Information Theory publishes papers concerned with the transmission, processing, and utilization of information.
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Videos on Information Theory
Find tutorials, lectures, keynotes and technical presentations on information theory
Research In Information Theory
Selected papers on information theory from our journals and conferences
IEEE JSAIT on the Information Theory Magazine
Shannon Bounds for Quadratic Rate-Distortion Problems
The Shannon lower bound has been the subject of several important contributions by Berger. This paper surveys Shannon bounds on rate-distortion problems under mean-squared error distortion with a particular emphasis on Berger’s techniques. Moreover, as a new result, the Gray-Wyner network is added to the canon of settings for which such bounds are known. In the Shannon bounding technique, elegant lower bounds are expressed in terms of the source entropy power.
IEEE JSAIT on the Information Theory Magazine
Computation of Binary Arithmetic Sum Over an Asymmetric Diamond Network
In this paper, the problem of zero-error network function computation is considered, where in a directed acyclic network, a single sink node is required to compute with zero error a function of the source messages that are separately generated by multiple source nodes. From the information-theoretic point of view, we are interested in the fundamental computing capacity, which is defined as the average number of times that the function can be computed with zero error for one use of the network.
IEEE JSAIT on the Information Theory Magazine
Low-Complexity Coding Techniques for Cloud Radio Access Networks
The problem of coding for the uplink and downlink of cloud radio access networks (C-RAN’s) with K users and L relays is considered. It is shown that low-complexity coding schemes that achieve any point in the rate-fronthaul region of joint coding and compression can be constructed starting from at most 4(K+L)-2 point-to-point codes designed for symmetric channels. This reduces the seemingly hard task of constructing good codes for C-RAN’s to the much better understood task of finding good codes for single-user channels.
IEEE JSAIT on the Information Theory Magazine
Erratum to “LightVeriFL: A Lightweight and Verifiable Secure Aggregation for Federated Learning”
This article addresses errors in [1]. Equation (2) contained an error wherein x was not bold. It is corrected below.
IEEE JSAIT on the Information Theory Magazine
JPEG Compliant Compression for DNN Vision
Conventional image compression techniques are primarily developed for the human visual system. However, with the extensive use of deep neural networks (DNNs) for computer vision, more and more images will be consumed by DNN-based intelligent machines, which makes it crucial to develop image compression techniques customized for DNN vision while being JPEG compliant. In this paper, we revisit the JPEG rate distortion theory for DNN vision. First, we propose a novel distortion measure, dubbed the sensitivity weighted error (SWE), for DNN vision.
IEEE JSAIT on the Information Theory Magazine
Throughput and Latency Analysis for Line Networks With Outage Links
Wireless communication links suffer from outage events caused by fading and interference. To facilitate a tractable analysis of network communication throughput and latency, we propose an outage link model to represent a communication link in the slow fading phenomenon. For a line-topology network with outage links, we study three types of intermediate network node schemes: random linear network coding, store-and-forward, and hop-by-hop retransmission. We provide the analytical formulas for the maximum throughputs and the end-to-end latency for each scheme.
IEEE JSAIT on the Information Theory Magazine
Addressing GAN Training Instabilities via Tunable Classification Losses
Generative adversarial networks (GANs), modeled as a zero-sum game between a generator (G) and a discriminator (D), allow generating synthetic data with formal guarantees. Noting that D is a classifier, we begin by reformulating the GAN value function using class probability estimation (CPE) losses. We prove a two-way correspondence between CPE loss GANs and f-GANs which minimize f-divergences. We also show that all symmetric f-divergences are equivalent in convergence.
IEEE JSAIT on the Information Theory Magazine
Information Velocity of Cascaded Gaussian Channels With Feedback
We consider a line network of nodes, connected by additive white noise channels, equipped with local feedback. We study the velocity at which information spreads over this network. For transmission of a data packet, we give an explicit positive lower bound on the velocity, for any packet size. Furthermore, we consider streaming, that is, transmission of data packets generated at a given average arrival rate. We show that a positive velocity exists as long as the arrival rate is below the individual Gaussian channel capacity, and provide an explicit lower bound.
IEEE JSAIT on the Information Theory Magazine
Long-Term Fairness in Sequential Multi-Agent Selection with Positive Reinforcement
While much of the rapidly growing literature on fair decision-making focuses on metrics for one-shot decisions, recent work has raised the intriguing possibility of designing sequential decision-making to positively impact long-term social fairness. In selection processes such as college admissions or hiring, biasing slightly towards applicants from under-represented groups is hypothesized to provide positive feedback that increases the pool of under-represented applicants in future selection rounds, thus enhancing fairness in the long term.
IEEE JSAIT on the Information Theory Magazine
Controlled privacy leakage propagation throughout overlapping grouped learning
Federated Learning (FL) is the standard protocol for collaborative learning. In FL, multiple workers jointly train a shared model. They exchange model updates calculated on their data, while keeping the raw data itself local. Since workers naturally form groups based on common interests and privacy policies, we are motivated to extend standard FL to reflect a setting with multiple, potentially overlapping groups.
Distinguished Lecturers
The Distinguished Lecturers program promotes interest in information theory by supporting chapters who wish to invite prominent information theory researchers to give talks at their events.
