  Black-box variational inference performance is sometimes hindered by the use
of gradient estimators with high variance. This variance comes from two sources
of randomness: Data subsampling and Monte Carlo sampling. While existing
control variates only address Monte Carlo noise, and incremental gradient
methods typically only address data subsampling, we propose a new "joint"
control variate that jointly reduces variance from both sources of noise. This
significantly reduces gradient variance, leading to faster optimization in
several applications.
