  Deep generative networks can simulate from a complex target distribution, by
minimizing a loss with respect to samples from that distribution. However,
often we do not have direct access to our target distribution - our data may be
subject to sample selection bias, or may be from a different but related
distribution. We present methods based on importance weighting that can
estimate the loss with respect to a target distribution, even if we cannot
access that distribution directly, in a variety of settings. These estimators,
which differentially weight the contribution of data to the loss function,
offer both theoretical guarantees and impressive empirical performance.
