Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Variance reduction for dependent sequences with applications to
  Stochastic Gradient MCMC   In this paper we propose a novel and practical variance reduction approach
for additive functionals of dependent sequences. Our approach combines the use
of control variates with the minimisation of an empirical variance estimate. We
analyse finite sample properties of the proposed method and derive finite-time
bounds of the excess asymptotic variance to zero. We apply our methodology to
Stochastic Gradient MCMC (SGMCMC) methods for Bayesian inference on large data
sets and combine it with existing variance reduction methods for SGMCMC. We
present empirical results carried out on a number of benchmark examples showing
that our variance reduction method achieves significant improvement as compared
to state-of-the-art methods at the expense of a moderate increase of
computational overhead.

12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph