Robustness and Diffusion Models

This lecture covers the mathematics behind robustness and diffusion models in data analysis, focusing on the challenges faced in training Generative Adversarial Networks (GANs). The instructor discusses the difficulties of GAN training, the application of algorithms to Gaussian distributions, and the complexities of nonconvex-nonconcave settings. The lecture delves into the concept of saddle points, the convergence of algorithms in minimax formulations, and the alternative proposal of mixed Nash equilibrium in game theory. Additionally, it explores the implications of Langevin Dynamics in robust reinforcement learning and the role of neural network architectures in learning natural distributions.