Algorithmic high-dimensional robust statistics / Ilias Diakonikolas, Daniel M. Kane.

Format:

Book

Author/Creator:

Diakonikolas, Ilias, author.

Kane, Daniel M., 1986- author.

Language:

English

Subjects (All):

Robust statistics--Data processing.

Robust statistics.

Computer algorithms.

Physical Description:

1 online resource (xvi, 283 pages) : digital, PDF file(s).

Place of Publication:

Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2023.

Summary:

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.

Contents:

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.

Notes:

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

Other Format:

Print version:

ISBN:

9781108943161 (ebook)

Access Restriction:

Restricted for use by site license.