  We analyze convergence rates of stochastic optimization procedures for
non-smooth convex optimization problems. By combining randomized smoothing
techniques with accelerated gradient methods, we obtain convergence rates of
stochastic optimization procedures, both in expectation and with high
probability, that have optimal dependence on the variance of the gradient
estimates. To the best of our knowledge, these are the first variance-based
rates for non-smooth optimization. We give several applications of our results
to statistical estimation problems, and provide experimental results that
demonstrate the effectiveness of the proposed algorithms. We also describe how
a combination of our algorithm with recent work on decentralized optimization
yields a distributed stochastic optimization algorithm that is order-optimal.

Optimization and Control;Machine Learning