Robust Machine Learning (Record no. 87808)

000 -LEADER
fixed length control field 04038nam a22005895i 4500
001 - CONTROL NUMBER
control field 978-981-97-0688-4
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730171753.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240404s2024 si | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9789819706884
-- 978-981-97-0688-4
082 04 - CLASSIFICATION NUMBER
Call Number 006.31
100 1# - AUTHOR NAME
Author Guerraoui, Rachid.
245 10 - TITLE STATEMENT
Title Robust Machine Learning
Sub Title Distributed Methods for Safe AI /
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2024.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XVII, 170 p. 12 illus., 11 illus. in color.
490 1# - SERIES STATEMENT
Series statement Machine Learning: Foundations, Methodologies, and Applications,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Chapter 1. Context & Motivation -- Chapter 2. Basics of Machine Learning -- Chapter 3. Federated Machine Learning -- Chapter 4. Fundamentals of Robust Machine Learning -- Chapter 5. Optimal Robustness -- Chapter 6. Practical Robustness. .
520 ## - SUMMARY, ETC.
Summary, etc Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms. Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area. .
700 1# - AUTHOR 2
Author 2 Gupta, Nirupam.
700 1# - AUTHOR 2
Author 2 Pinot, Rafael.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-981-97-0688-4
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Singapore :
-- Springer Nature Singapore :
-- Imprint: Springer,
-- 2024.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine learning.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer security.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Multiagent systems.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Cloud Computing.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine Learning.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Principles and Models of Security.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Multiagent Systems.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Cloud Computing.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2730-9916
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-- ZDB-2-SCS
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-- ZDB-2-SXCS

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