  We propose an approach to construction of robust non-Euclidean iterative
algorithms for convex composite stochastic optimization based on truncation of
stochastic gradients. For such algorithms, we establish sub-Gaussian confidence
bounds under weak assumptions about the tails of the noise distribution in
convex and strongly convex settings. Robust estimates of the accuracy of
general stochastic algorithms are also proposed.
