Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Constrained Bayesian Optimization for Automatic Chemical Design   Automatic Chemical Design is a framework for generating novel molecules with
optimized properties. The original scheme, featuring Bayesian optimization over
the latent space of a variational autoencoder, suffers from the pathology that
it tends to produce invalid molecular structures. First, we demonstrate
empirically that this pathology arises when the Bayesian optimization scheme
queries latent points far away from the data on which the variational
autoencoder has been trained. Secondly, by reformulating the search procedure
as a constrained Bayesian optimization problem, we show that the effects of
this pathology can be mitigated, yielding marked improvements in the validity
of the generated molecules. We posit that constrained Bayesian optimization is
a good approach for solving this class of training set mismatch in many
generative tasks involving Bayesian optimization over the latent space of a
variational autoencoder.

17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci