This book provides a modern treatment of compositional optimization techniques and their applications in advanced machine learning, including predictive, generative, and representation learning. It introduces key algorithms, theoretical foundations, and practical insights across a wide spectrum of learning paradigms.

Chapters

About the Author

Tianbao Yang is a Professor and Stephen Horn ‘79 Engineering Excellence Chair at CSE department of Texas A&M University, where he directs the lab of Optimization for Machine learning and AI (OptMAI Lab). His research interests center around optimization, machine learning and efficient AI with applications in medicine. Before joining TAMU, he was an assistant professor and then tenured Dean’s Excellence associate professor at the Computer Science Department of the University of Iowa from 2014 to 2022. Before that, he worked in Silicon Valley as Machine Learning Researcher for two years at GE Research and NEC Labs. He received the Best Student Paper Award of COLT in 2012, and the NSF Career Award in 2019.

He is recognized for his contributions to optimization in ML/AI. He is the founder of the widely used LibAUC library. His NeurIPS 2013 paper on distributed optimization pioneered the ideas of local updates and model averaging, which later became fundamental to federated learning. He also introduced the empirical X-risk minimization framework along with efficient algorithms for solving it, addressing decades-long open problems in machine learning and forming the foundation of the LibAUC library. He is the author of the book “Compositional Optimization for Advanced Machine Learning”. He is associate editor of multiple journals, including IEEE Transactions on Pattern Analysis and Machine Intelligence and ACM Computing Surveys.

For questions, collaborations, or updates, please email tianbao-yang@tamu.edu.