Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines

ICML 2018 Bin GuZhouyuan HuoCheng DengHeng Huang

Asynchronous parallel stochastic gradient optimization has been playing a pivotal role to solve large-scale machine learning problems in big data applications. Zeroth-order (derivative-free) methods estimate the gradient only by two function evaluations, thus have been applied to solve the problems where the explicit gradient calculations are computationally expensive or infeasible... (read more)

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