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Algorithmic high-dimensional robust statistics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Algorithmic high-dimensional robust statistics/ Ilias Diakonikolas, Daniel M. Kane.
Author:	Diakonikolas, Ilias.
other author:	Kane, Daniel M.,
Published:	Cambridge, United Kingdom ;Cambridge University Press, : 2023.,
Description:	xvi, 283 p. :ill., digital ;23 cm.
Notes:	Title from publisher's bibliographic system (viewed on 30 Aug 2023).
[NT 15003449]:	Introduction to robust statistics -- Efficient high-dimensional robust mean estimation -- Algorithmic refinements in robust mean estimation -- Robust covariance estimation -- List-decodable learning -- Robust estimation via higher moments -- Robust supervised learning -- Information-computation trade-offs in high-dimensional robust statistics.
Subject:	Robust statistics - Data processing. -
Online resource:	https://doi.org/10.1017/9781108943161
ISBN:	9781108943161

Algorithmic high-dimensional robust statistics
Diakonikolas, Ilias.

Algorithmic high-dimensional robust statistics[electronic resource] /Ilias Diakonikolas, Daniel M. Kane. - Cambridge, United Kingdom ;Cambridge University Press,2023. - xvi, 283 p. :ill., digital ;23 cm.

Title from publisher's bibliographic system (viewed on 30 Aug 2023).

Introduction to robust statistics -- Efficient high-dimensional robust mean estimation -- Algorithmic refinements in robust mean estimation -- Robust covariance estimation -- List-decodable learning -- Robust estimation via higher moments -- Robust supervised learning -- Information-computation trade-offs in high-dimensional robust statistics.

Robust statistics is the study of designing estimators that perform well even when the dataset significantly deviates from the idealized modeling assumptions, such as in the presence of model misspecification or adversarial outliers in the dataset. The classical statistical theory, dating back to pioneering works by Tukey and Huber, characterizes the information-theoretic limits of robust estimation for most common problems. A recent line of work in computer science gave the first computationally efficient robust estimators in high dimensions for a range of learning tasks. This reference text for graduate students, researchers, and professionals in machine learning theory, provides an overview of recent developments in algorithmic high-dimensional robust statistics, presenting the underlying ideas in a clear and unified manner, while leveraging new perspectives on the developed techniques to provide streamlined proofs of these results. The most basic and illustrative results are analyzed in each chapter, while more tangential developments are explored in the exercises.

ISBN: 9781108943161Subjects--Topical Terms:

3729335
Robust statistics
--Data processing.

LC Class. No.: QA276.4 / .D53 2023

Dewey Class. No.: 519.502855369

Algorithmic high-dimensional robust statistics
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260 $a Cambridge, United Kingdom ; $a New York, NY : $b Cambridge University Press, $c 2023.
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500 $a Title from publisher's bibliographic system (viewed on 30 Aug 2023).
505 0 $a Introduction to robust statistics -- Efficient high-dimensional robust mean estimation -- Algorithmic refinements in robust mean estimation -- Robust covariance estimation -- List-decodable learning -- Robust estimation via higher moments -- Robust supervised learning -- Information-computation trade-offs in high-dimensional robust statistics.
520 $a Robust statistics is the study of designing estimators that perform well even when the dataset significantly deviates from the idealized modeling assumptions, such as in the presence of model misspecification or adversarial outliers in the dataset. The classical statistical theory, dating back to pioneering works by Tukey and Huber, characterizes the information-theoretic limits of robust estimation for most common problems. A recent line of work in computer science gave the first computationally efficient robust estimators in high dimensions for a range of learning tasks. This reference text for graduate students, researchers, and professionals in machine learning theory, provides an overview of recent developments in algorithmic high-dimensional robust statistics, presenting the underlying ideas in a clear and unified manner, while leveraging new perspectives on the developed techniques to provide streamlined proofs of these results. The most basic and illustrative results are analyzed in each chapter, while more tangential developments are explored in the exercises.
650 0 $a Robust statistics $x Data processing. $3 3729335
650 0 $a Computer algorithms. $3 523872
700 1 $a Kane, Daniel M., $d 1986- $3 3729334
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