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Robust Machine Learning

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Fundamentals Of Robust Machine Learning

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Author : Resve A. Saleh
language : en
Publisher: John Wiley & Sons
Release Date : 2025-05-28

Fundamentals Of Robust Machine Learning written by Resve A. Saleh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-28 with Computers categories.

An essential guide for tackling outliers and anomalies in machine learning and data science. In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few. Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models. Fundamentals of Robust Machine Learning readers will also find: A blend of robust statistics and machine learning principles Detailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detection Python code with immediate application to data science problems Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.

Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies

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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2019-08-22

Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies written by National Academies of Sciences, Engineering, and Medicine and has been published by National Academies Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-08-22 with Computers categories.

The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.

Robust Machine Learning

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Author : Rachid Guerraoui
language : en
Publisher: Springer Nature
Release Date : 2024-04-04

Robust Machine Learning written by Rachid Guerraoui and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-04 with Mathematics categories.

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.

Robust Machine Learning Algorithms And Systems For Detection And Mitigation Of Adversarial Attacks And Anomalies

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Author : National Academies of Sciences, Engineering, and Medicine
language : en
Publisher: National Academies Press
Release Date : 2019-08-22

Adversarial Robustness For Machine Learning

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Author : Pin-Yu Chen
language : en
Publisher: Academic Press
Release Date : 2022-08-20

Adversarial Robustness For Machine Learning written by Pin-Yu Chen and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-20 with Computers categories.

Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on adversarial attack, defense and verification. Sections cover adversarial attack, verification and defense, mainly focusing on image classification applications which are the standard benchmark considered in the adversarial robustness community. Other sections discuss adversarial examples beyond image classification, other threat models beyond testing time attack, and applications on adversarial robustness. For researchers, this book provides a thorough literature review that summarizes latest progress in the area, which can be a good reference for conducting future research. In addition, the book can also be used as a textbook for graduate courses on adversarial robustness or trustworthy machine learning. While machine learning (ML) algorithms have achieved remarkable performance in many applications, recent studies have demonstrated their lack of robustness against adversarial disturbance. The lack of robustness brings security concerns in ML models for real applications such as self-driving cars, robotics controls and healthcare systems. - Summarizes the whole field of adversarial robustness for Machine learning models - Provides a clearly explained, self-contained reference - Introduces formulations, algorithms and intuitions - Includes applications based on adversarial robustness

Distributionally Robust Learning

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Author : Ruidi Chen
language : en
Publisher:
Release Date : 2020

Distributionally Robust Learning written by Ruidi Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic books categories.

This monograph provides insight into a technique that has gained a lot of recent interest in developing robust supervised learning solutions that are founded in sound mathematical principles. It will be enlightening for researchers, practitioners and students in the optimization of machine learning systems.

Automated Machine Learning

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Author : Frank Hutter
language : en
Publisher: Springer
Release Date : 2019-05-17

Automated Machine Learning written by Frank Hutter and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-17 with Computers categories.

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Adversarial Machine Learning

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Author : Yevgeniy Vorobeychik
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2018-08-08

Adversarial Machine Learning written by Yevgeniy Vorobeychik and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-08 with Computers categories.

This is a technical overview of the field of adversarial machine learning which has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research. The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicious objects they develop. Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.

Robust Data Mining

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Author : Petros Xanthopoulos
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-11-28

Robust Data Mining written by Petros Xanthopoulos and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-28 with Mathematics categories.

Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field.

Malware Detection

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Author : Mihai Christodorescu
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-03-06

Malware Detection written by Mihai Christodorescu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-06 with Computers categories.

This book captures the state of the art research in the area of malicious code detection, prevention and mitigation. It contains cutting-edge behavior-based techniques to analyze and detect obfuscated malware. The book analyzes current trends in malware activity online, including botnets and malicious code for profit, and it proposes effective models for detection and prevention of attacks using. Furthermore, the book introduces novel techniques for creating services that protect their own integrity and safety, plus the data they manage.

Robust Machine Learning

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