Adversarial Machine Learning

This lecture covers adversarial machine learning and generative adversarial networks, focusing on the mathematics behind data optimization, empirical risk minimization, and the challenges posed by adversarial examples. The instructor delves into the theory and computation of adversarial attacks, the formulation of adversarial training, and the practical implementation of generative adversarial networks. The lecture also explores the duality of 1-Wasserstein distance, integral probability metrics, and the stochastic training of Wasserstein GANs. Various optimization problems, such as weight clipping and gradient penalties, are discussed in the context of enforcing Lipschitz constraints in neural networks.