Non-Convex Min-Max Optimization: Provable Algorithms and Applications in Machine Learning

4 Oct 2018Hassan RafiqueMingrui LiuQihang LinTianbao Yang

Min-max saddle-point problems have broad applications in many tasks in machine learning, e.g., distributionally robust learning, learning with non-decomposable loss, or learning with uncertain data. Although convex-concave saddle-point problems have been broadly studied with efficient algorithms and solid theories available, it remains a challenge to design provably efficient algorithms for non-convex saddle-point problems, especially when the objective function involves an expectation or a large-scale finite sum... (read more)

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