  Many important problems in science and engineering, such as drug design,
involve optimizing an expensive black-box objective function over a complex,
high-dimensional, and structured input space. Although machine learning
techniques have shown promise in solving such problems, existing approaches
substantially lack sample efficiency. We introduce an improved method for
efficient black-box optimization, which performs the optimization in the
low-dimensional, continuous latent manifold learned by a deep generative model.
In contrast to previous approaches, we actively steer the generative model to
maintain a latent manifold that is highly useful for efficiently optimizing the
objective. We achieve this by periodically retraining the generative model on
the data points queried along the optimization trajectory, as well as weighting
those data points according to their objective function value. This weighted
retraining can be easily implemented on top of existing methods, and is
empirically shown to significantly improve their efficiency and performance on
synthetic and real-world optimization problems.
