Learning Theory of Transformers: Generalization and Optimization of In-Context Learning

Watch a 46-minute lecture from the Simons Institute where Professor Taiji Suzuki from the University of Tokyo explores the theoretical foundations of Transformer models, with a specific focus on in-context learning capabilities. Dive into statistical efficiency and approximation ability concepts, discovering how Transformers achieve minimax optimality in in-context learning scenarios and their advantages over non-pretrained methods. Explore optimization theory aspects, including how nonlinear feature learning operates with optimization guarantees, the strict-saddle property in mean field settings, and computational efficiency evaluation based on information exponents for single index models. Gain valuable insights into the Unknown Futures of Generalization through this theoretical analysis of Transformer architecture capabilities.