In this talk, we present a regularized variant of the alternating direction method of multipliers (ADMM) for solving linearly constrained convex programs. The pointwise iteration-complexity of the new variant is better than the corresponding one for the standard ADMM method and, up to a logarithmic term, is identical to the ergodic iteration-complexity of the latter method. We discuss how this regularized ADMM can be seen as an instance of a regularized hybrid proximal extragradient framework whose error condition at each iteration includes both a relative error and a summable error.