東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Geometric Techniques in Multiterminal Communication and Estimation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Geometric Techniques in Multiterminal Communication and Estimation./
Author:	Barnes, Leighton Pate.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	172 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
Subject:	Language. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28483296
ISBN:	9798505571798

Geometric Techniques in Multiterminal Communication and Estimation.
Barnes, Leighton Pate.

Geometric Techniques in Multiterminal Communication and Estimation. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 172 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Since its inception in 1948, one of the main goals in information theory has been to extend its original scope of point-to-point communication to include networks of nodes exchanging information. Towards this goal, we develop tools from high-dimensional geometry to analyze the fundamental limits of communication and statistical estimation tasks in a networked setting.In the first half of the thesis, we describe a problem in network communications -- the relay channel -- and prove a new upper bound on the capacity of this channel which resolves an open problem posed by Thomas Cover in "Open Problems in Communication and Computation", Springer-Verlag, 1987. The proof is highly geometric, with its main ingredient being a new isoperimetric result on high-dimensional spheres that builds on a Riesz-type rearrangement inequality.In the second half of the thesis, we consider a collection of networked statistical estimation problems modeling bandwidth and privacy constraints in distributed and federated learning systems. In these problems, data is distributed across many nodes in a network and must be communicated to a centralized estimator under communication, privacy, or mutual information constraints. We show how a geometric interpretation of Fisher information from the processed statistical samples can derive tight minimax lower bounds for many distributed estimation problems of interest.

ISBN: 9798505571798Subjects--Topical Terms:

643551
Language.

Geometric Techniques in Multiterminal Communication and Estimation.
LDR:02505nmm a2200349 4500 001 2349814
005 20221010063628.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798505571798
035 $a (MiAaPQ)AAI28483296
035 $a (MiAaPQ)STANFORDvr693cz0711
035 $a AAI28483296
040 $a MiAaPQ $c MiAaPQ
100 1 $a Barnes, Leighton Pate. $3 3689233
245 1 0 $a Geometric Techniques in Multiterminal Communication and Estimation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 172 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Ozgur, Ayfer;Osgood, Brad;Weissman, Tsachy.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Since its inception in 1948, one of the main goals in information theory has been to extend its original scope of point-to-point communication to include networks of nodes exchanging information. Towards this goal, we develop tools from high-dimensional geometry to analyze the fundamental limits of communication and statistical estimation tasks in a networked setting.In the first half of the thesis, we describe a problem in network communications -- the relay channel -- and prove a new upper bound on the capacity of this channel which resolves an open problem posed by Thomas Cover in "Open Problems in Communication and Computation", Springer-Verlag, 1987. The proof is highly geometric, with its main ingredient being a new isoperimetric result on high-dimensional spheres that builds on a Riesz-type rearrangement inequality.In the second half of the thesis, we consider a collection of networked statistical estimation problems modeling bandwidth and privacy constraints in distributed and federated learning systems. In these problems, data is distributed across many nodes in a network and must be communicated to a centralized estimator under communication, privacy, or mutual information constraints. We show how a geometric interpretation of Fisher information from the processed statistical samples can derive tight minimax lower bounds for many distributed estimation problems of interest.
590 $a School code: 0212.
650 4 $a Language. $3 643551
650 4 $a Accuracy. $3 3559958
650 4 $a Datasets. $3 3541416
650 4 $a Communication. $3 524709
650 4 $a Data processing. $3 680224
650 4 $a Information processing. $3 3561808
650 4 $a Privacy. $3 528582
650 4 $a Inequality. $3 3560119
650 4 $a Information theory. $3 542527
650 4 $a Geometry. $3 517251
650 4 $a Parameter estimation. $3 567557
650 4 $a Applied mathematics. $3 2122814
650 4 $a Artificial intelligence. $3 516317
650 4 $a Mathematics. $3 515831
690 $a 0364
690 $a 0800
690 $a 0459
690 $a 0405
690 $a 0679
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 83-02B.
790 $a 0212
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28483296