Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities

NeurIPS 2018 Yunwen LeiKe Tang

We study stochastic composite mirror descent, a class of scalable algorithms able to exploit the geometry and composite structure of a problem. We consider both convex and strongly convex objectives with non-smooth loss functions, for each of which we establish high-probability convergence rates optimal up to a logarithmic factor... (read more)

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