Provably effective algorithms for min-max optimization

Lei, Qi, 1992-, 0000-0003-0634-6435, Austin, The University Of Texas At, Inderjit S. Dhillon, Alexandros G. Dimakis
Many fundamental machine learning tasks can be formulated as min-max optimization. This motivates us to design effective and efficient first-order methods that provably converge to the global min-max points. For this purpose, this thesis focuses on designing practical algorithms for several specific machine learning tasks. We considered some different settings: unconstrained or constrained strongly-convex (strongly-)concave, constrained convex-concave, and nonconvex-concave problems. We tackle
more » ... he following concrete questions by studying the above problems: 1. Can we reformulate a single minimization problem to two-player games to help reduce the computational complexity of finding global optimal points? 2. Can projection-free algorithms achieve last-iterate convergence for constrained min-max optimization problems with the convex-concave landscape? 3. Can we show that stochastic gradient descent-ascent, a method commonly used in practice for GAN training, actually finds global optima and can learn a target distribution? We make progress on these questions by proposing practical algorithms with theoretical guarantees. We also present extensive empirical studies to verify the effectiveness of our proposed methods.
doi:10.26153/tsw/10153 fatcat:ylryreuz35bt3db4naamj57f3m