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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 learningtechniques have shown promise in solving such problems, existing approachessubstantially lack sample efficiency. We introduce an improved method forefficient black-box optimization, which performs the optimization in thelow-dimensional, continuous latent manifold learned by a deep generative model.In contrast to previous approaches, we actively steer the generative model tomaintain a latent manifold that is highly useful for efficiently optimizing theobjective. We achieve this by periodically retraining the generative model onthe data points queried along the optimization trajectory, as well as weightingthose data points according to their objective function value. This weightedretraining can be easily implemented on top of existing methods, and isempirically shown to significantly improve their efficiency and performance onsynthetic and real-world optimization problems.

Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining