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
- Starter: Convex Optimization
- Introduction: Advanced Machine Learning
- Basics: Stochastic Optimization
- Foundations: Stochastic Compositional Optimization
- Advances: Finite-sum Coupled Compositional Optimization
- Applications: Learning Predictive, Generative, and Representation Models
Contact
For questions, collaborations, or updates, please email tianbao-yang@tamu.edu.