JSAIT CFP: Estimation and Inference

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This special issue will focus on the intersection of Information theory with estimation and inference. Information Theory has provided powerful tools as well as deep insights into optimal procedures for statistical inference and estimation. The application of these tools include characterization of optimal error probabilities in hypothesis testing, determination of minimax rates of convergence for estimation problems, analysis of message-passing and other efficient algorithms, as well as demonstrating the equivalence of different estimation problems. This issue will illuminate new connections between information theory, statistical inference, and estimation, as well as highlight applications where information-theoretic tools for inference and estimation have proved fruitful in a wide range of areas including signal processing, data mining, machine learning, pattern and image recognition, computational neuroscience, bioinformatics and cryptography.

IEEE Journal on Selected Areas in Information Theory (JSAIT)
Editor-in-Chief: Andrea Goldsmith (Stanford University)

Call for Papers

Prospective authors are invited to submit original manuscripts on topics within this broad scope including, but not limited to:

  • Matrix and Tensor Estimation
  • Graphical Models
  • Optimization for Estimation and Inference
  • High-dimensional Statistics
  • Privacy
  • Generalization Bounds and Connections to Learning Theory
  • Functional Estimation
  • Message-Passing Algorithms
  • Bayesian Inference
  • Black-box Uncertainty Quantification and Inference 
  • Combinatorial Estimation Problems

Guest Editors

Lead Guest Editor

  • Devavrat Shah (MIT): devavrat@mit.edu 

Guest Editors

  • Guy Bresler (MIT): guy@mit.edu
  • John Duchi (Stanford): jduchi@stanford.edu
  • Po-Ling Loh (Univ of Wisconsin): ploh@stat.wisc.edu
  • Ryan Tibshirani (CMU): ryantibs@cmu.edu
  • Yihong Wu (Yale): yihong.wu@yale.edu
  • Christina Lee Yu (Cornell): cleeyu@cornell.edu

Senior Editor Advisers

  • Emmanuel Candes (Stanford)
  • Andrea Montanari (Stanford)

Submission Guidelines

Prospective authors must follow the IEEE Journal on Selected Areas in Information Theory guidelines regarding the manuscript and its format. For details and templates, please refer to the IEEE Journal on Selected Areas in Information Theory Author Information webpage. All papers should be submitted through Scholar One according to the following schedule:

Important Dates

Manuscript Due: May 15, 2020 (Extended from May 1)

Acceptance Notification: October 15, 2020

Final to Publisher: November 5, 2020

Expected Publication: November/December 2020

Manuscript Submission Website: https://mc.manuscriptcentral.com/jsait-ieee

Download this call for papers as a PDF.