Online Convex Optimization: From Proving Minimax Theorems to Training Machine Learning Models by Playing Repeated Games
Modeling sequential decision problems as repeated games has a long history in statistics, information theory and machine learning. Using the modern unified setting of online convex optimization, I will introduce several canonical results and highlight applications that range from an elementary proof of von Neumann's minimax theorem to optimization of (deep) learning models in machine learning.
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