  In this paper, we study a simple algorithm to construct asymptotically valid
confidence regions for model parameters using the batch means method. The main
idea is to cancel out the covariance matrix which is hard/costly to estimate.
In the process of developing the algorithm, we establish process-level
functional central limit theorem for Polyak-Ruppert averaging based stochastic
gradient descent estimators. We also extend the batch means method to
accommodate more general batch size specifications.
