  In this work, we consider a sequence of stochastic optimization problems
following a time-varying distribution via the lens of online optimization.
Assuming that the loss function satisfies the Polyak-{\L}ojasiewicz condition,
we apply online stochastic gradient descent and establish its dynamic regret
bound that is composed of cumulative distribution drifts and cumulative
gradient biases caused by stochasticity. The distribution metric we adopt here
is Wasserstein distance, which is well-defined without the absolute continuity
assumption or with a time-varying support set. We also establish a regret bound
of online stochastic proximal gradient descent when the objective function is
regularized. Moreover, we show that the above framework can be applied to the
Conditional Value-at-Risk (CVaR) learning problem. Particularly, we improve an
existing proof on the discovery of the PL condition of the CVaR problem,
resulting in a regret bound of online stochastic gradient descent.
