Bo-Wen Li

Accelerated stochastic first-order method for convex optimization under heavy-tailed noise

This paper establishes that an accelerated stochastic proximal subgradient method achieves a first-order oracle complexity for finding an approximate optimal solution in expectation that is universally optimal for smooth, weakly smooth, and nonsmooth convex optimization, as well as for stochastic convex optimization un...