Explore the optimization landscape of two-layer neural networks in this 58-minute seminar on theoretical machine learning presented by Rong Ge from Duke University. Delve into topics such as non-convexity, saddle points, and local-optimizable functions. Examine results for symmetric distributions and gain insights into optimization landscapes with symmetric input distributions. Learn about high-level ideas and interpolation techniques as applied to two-layer neural networks. This comprehensive talk, delivered at the Institute for Advanced Study, offers a deep dive into the mathematical foundations of neural network optimization.