Fundamental Limitations in Sequential Prediction and Recursive Algorithms: Lp Bounds via an Entropic Analysis

Song Fang, Quanyan Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we obtain fundamental Lp bounds in sequential prediction and recursive algorithms via an entropic analysis. Both classes of problems are examined by investigating the underlying entropic relationships of the data and/or noises involved, and the derived lower bounds may all be quantified in a conditional entropy characterization. We also study the conditions to achieve the generic bounds from an innovations' viewpoint.

Original languageEnglish (US)
Title of host publication2020 54th Annual Conference on Information Sciences and Systems, CISS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728140841
DOIs
StatePublished - Mar 2020
Event54th Annual Conference on Information Sciences and Systems, CISS 2020 - Princeton, United States
Duration: Mar 18 2020Mar 20 2020

Publication series

Name2020 54th Annual Conference on Information Sciences and Systems, CISS 2020

Conference

Conference54th Annual Conference on Information Sciences and Systems, CISS 2020
CountryUnited States
CityPrinceton
Period3/18/203/20/20

Keywords

  • Information theory
  • entropy
  • innovations
  • machine learning
  • recursive algorithm
  • sequential prediction

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence

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    Fang, S., & Zhu, Q. (2020). Fundamental Limitations in Sequential Prediction and Recursive Algorithms: Lp Bounds via an Entropic Analysis. In 2020 54th Annual Conference on Information Sciences and Systems, CISS 2020 [9086198] (2020 54th Annual Conference on Information Sciences and Systems, CISS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS48834.2020.1570617026