00991nam0M2200361--I450-99000342005020331620100630100643.00-19-511821-9000342005USA01000342005(ALEPH)000342005USA0100034200520100630d1998----||itac01 baengUS||||||||001yyWhy we watchthe attractions of violent entertainmentedited by Jeffrey GoldsteinNew YorkOxford university press1998X, 270 p.ill.24 cm00120012001001-------2001Sport violenzaBNCF344.045GOLDSTEIN,Jeffrey H.ITICCUISBD20040213990003420050203316344.045 GOL16557 DSSPBKDSSPDSSP21020100630USA011006Why we watch1109616UNISA03831nam 22006735 450 991084758340332120250807135836.0981-9706-88-210.1007/978-981-97-0688-4(CKB)31367761900041(MiAaPQ)EBC31267082(Au-PeEL)EBL31267082(MiAaPQ)EBC31251670(Au-PeEL)EBL31251670(DE-He213)978-981-97-0688-4(EXLCZ)993136776190004120240404d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierRobust Machine Learning Distributed Methods for Safe AI /by Rachid Guerraoui, Nirupam Gupta, Rafael Pinot1st ed. 2024.Singapore :Springer Nature Singapore :Imprint: Springer,2024.1 online resource (0 pages)Machine Learning: Foundations, Methodologies, and Applications,2730-9916981-9706-87-4 Includes bibliographical references.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. .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. .Machine Learning: Foundations, Methodologies, and Applications,2730-9916Machine learningComputer securityMultiagent systemsCloud computingMachine LearningPrinciples and Models of SecurityMultiagent SystemsCloud ComputingMachine learning.Computer security.Multiagent systems.Cloud computing.Machine Learning.Principles and Models of Security.Multiagent Systems.Cloud Computing.006.3Guerraoui Rachid1311091Gupta NirupamPinot RafaelMiAaPQMiAaPQMiAaPQBOOK9910847583403321Robust Machine Learning4236389UNINA