We revisit an important class of composite stochastic minimization problems that often arises from empirical risk minimization settings, such as Lasso, Ridge Regression, and Logistic Regression. We present a new algorithm UniVR based on stochastic gradient descent with variance reduction. Our algorithm supports non-→