Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sample-Efficient Optimization in the Latent Space of Deep Generative
  Models via Weighted Retraining   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.

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The categories of this paper are: cond-mat
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The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
15
The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ex