SADAGRAD: Strongly Adaptive Stochastic Gradient Methods

ICML 2018 Zaiyi ChenYi XuEnhong ChenTianbao Yang

Although the convergence rates of existing variants of ADAGRAD have a better dependence on the number of iterations under the strong convexity condition, their iteration complexities have a explicitly linear dependence on the dimensionality of the problem. To alleviate this bad dependence, we propose a simple yet novel variant of ADAGRAD for stochastic (weakly) strongly convex optimization... (read more)

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