Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Variational ABC   Approximate Bayesian Computation (ABC) is a framework for performing
likelihood-free posterior inference for simulation models. Stochastic
Variational inference (SVI) is an appealing alternative to the inefficient
sampling approaches commonly used in ABC. However, SVI is highly sensitive to
the variance of the gradient estimators, and this problem is exacerbated by
approximating the likelihood. We draw upon recent advances in variance
reduction for SV and likelihood-free inference using deterministic simulations
to produce low variance gradient estimators of the variational lower-bound. By
then exploiting automatic differentiation libraries we can avoid nearly all
model-specific derivations. We demonstrate performance on three problems and
compare to existing SVI algorithms. Our results demonstrate the correctness and
efficiency of our algorithm.

2
The categories of this paper are: math
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
1
The categories of this paper are: cs
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas