Название: Mean Field Guided Machine Learning
Автор: Yuhan Kang, Hao Gao, Zhu Han
Издательство: Springer
Год: 2025
Страниц: 159
Язык: английский
Формат: pdf (true), epub
b]Размер[/b]: 17.45 MB

This book explores the integration of Mean Field Game (MFG) theory with Machine Learning (ML), presenting both theoretical foundations and practical applications. Drawing from extensive research, it provides insights into how MFG can improve various ML techniques, including Supervised Learning, Reinforcement Learning, and Federated Learning.

MFG theory and ML are converging to address critical challenges in high-dimensional spaces and multi-agent systems. While ML has transformed industries by leveraging vast data and computational power, scalability and robustness remain key concerns. MFG theory, which models large populations of interacting agents, offers a mathematical framework to simplify and optimize complex systems, enhancing ML’s efficiency and applicability.

By bridging these two fields, this book aims to drive innovation in scalable and robust Machine Learning. The integration of MFG with ML not only expands research possibilities but also paves the way for more adaptive and intelligent systems. Through this work, the authors hope to inspire further exploration and development in this promising interdisciplinary domain. With case studies and real-world examples, this book serves as a guide for researchers and students in communications and networks seeking to harness MFG’s potential in advancing ML. Industry managers, practitioners and government research workers in the fields of communications and networks will find this book a valuable resource as well.

Machine Learning (ML) has revolutionized numerous industries, transforming how we approach complex problems by leveraging vast amounts of data and advanced computational techniques. However, as powerful as these technologies have become, they still face significant challenges, particularly in high-dimensional spaces and multi-agent systems. The need to manage complexity, enhance scalability, and maintain robustness in increasingly sophisticated applications has never been greater.

In this book, we explore the intersection of Mean Field Game (MFG) theory and Machine Learning, an innovative approach that holds promise for addressing these pressing challenges. MFG theory, which models the collective behavior of large populations of interacting agents, provides powerful mathematical tools for simplifying and optimizing complex systems. By integrating MFG theory with ML, we can enhance the efficiency, scalability, and robustness of Machine Learning models, opening new avenues for research and application development.

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