2 Frameworkandassumptions 2.1 Stochasticoptimizationundertimedrift ThroughoutSections2-4,weconsiderthesequenceofstochasticoptimizationproblems min

Our results concisely explain the interplay between the learning rate, the noise variance in the gradient oracle, and the strength ofthetime drift. The high-probability results merely assume that thegradient noise and time drift have light tails. Moreover, none of the results require the objectives to have bounded domains.