Powered stochastic optimization with hypergradient descent for large-scale learning systems

Zhuang Yang, Xiaotian Li

Abstract: Highlights•We propose a type of adaptive powered stochastic optimization algorithms.•The theoretical analysis of the resulting algorithms is established.•Experiments on machine learning tasks verify the efficacy of the algorithms.