Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variational Boosting: Iteratively Refining Posterior Approximations   We propose a black-box variational inference method to approximate
intractable distributions with an increasingly rich approximating class. Our
method, termed variational boosting, iteratively refines an existing
variational approximation by solving a sequence of optimization problems,
allowing the practitioner to trade computation time for accuracy. We show how
to expand the variational approximating class by incorporating additional
covariance structure and by introducing new components to form a mixture. We
apply variational boosting to synthetic and real statistical models, and show
that resulting posterior inferences compare favorably to existing posterior
approximation algorithms in both accuracy and efficiency.

17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn