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Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.
Explores the history, theory, and applications of optimal transport in various fields, showcasing its importance in solving complex mathematical problems.
Covers the practical implementation and applications of adversarial training, Generative Adversarial Networks, distance between distributions, and enforcing 1-Lipschitz in GANs.
Explores causal discovery using latent variable models, emphasizing the challenges and solutions in inferring causal relationships from non-Gaussian data.
