Unlock: Open Problems in ML Theory

A curated list of genuinely open problems in machine learning theory: why overparameterized networks generalize, the right complexity measure for deep learning, feature learning beyond NTK, why scaling laws hold, emergent abilities, transformer-specific theory, and post-training theory.