About
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
- Chapter 1: Starter: Convex Optimization
- Chapter 2: Introduction: Advanced Machine Learning
- Chapter 3: Basics: Stochastic Optimization
- Chapter 4: Foundations: Stochastic Compositional Optimization
- Chapter 5: Advances: Finite-sum Coupled Compositional Optimization
- Chapter 6: Applications: Learning Predictive, Generative, and Representation Models
About the Author
Tianbao Yang is a Professor and Herbert H. Richardson Faculty Fellow 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 AI with applications in computer vision, NLP, trustworthy AI and 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 the founder of the widely used LibAUC library. He is associate editor of multiple journals, including IEEE Transactions on Pattern Analysis and Machine Intelligence.
For questions, collaborations, or updates, please email tianbao-yang@tamu.edu.