Universal Stagewise Learning for Non-Convex Problems with Convergence on Averaged Solutions

ICLR 2019 Zaiyi ChenZhuoning YuanJinfeng YiBowen ZhouEnhong ChenTianbao Yang

Although stochastic gradient descent (SGD) method and its variants (e.g., stochastic momentum methods, AdaGrad) are the choice of algorithms for solving non-convex problems (especially deep learning), there still remain big gaps between the theory and the practice with many questions unresolved. For example, there is still a lack of theories of convergence for SGD and its variants that use stagewise step size and return an averaged solution in practice... (read more)

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