|Seminar on Theoretical Machine Learning|
|Topic:||Nonconvex Minimax Optimization|
|Affiliation:||Princeton University; Member, School of Mathematics|
|Date:||Wednesday, November 20|
|Time/Room:||12:00pm - 1:30pm/Dilworth Room|
Minimax optimization, especially in its general nonconvex formulation, has found extensive applications in modern machine learning, in settings such as generative adversarial networks (GANs) and adversarial training. It brings a series of unique challenges in addition to those that already persist in nonconvex minimization problems. This talk will cover a set of new phenomena, open problems, and recent results in this emerging field.