  Bayesian experimental design (BED) is to answer the question that how to
choose designs that maximize the information gathering. For implicit models,
where the likelihood is intractable but sampling is possible, conventional BED
methods have difficulties in efficiently estimating the posterior distribution
and maximizing the mutual information (MI) between data and parameters. Recent
work proposed the use of gradient ascent to maximize a lower bound on MI to
deal with these issues. However, the approach requires a sampling path to
compute the pathwise gradient of the MI lower bound with respect to the design
variables, and such a pathwise gradient is usually inaccessible for implicit
models. In this paper, we propose a novel approach that leverages recent
advances in stochastic approximate gradient ascent incorporated with a smoothed
variational MI estimator for efficient and robust BED. Without the necessity of
pathwise gradients, our approach allows the design process to be achieved
through a unified procedure with an approximate gradient for implicit models.
Several experiments show that our approach outperforms baseline methods, and
significantly improves the scalability of BED in high-dimensional problems.
