Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient and Generalizable Tuning Strategies for Stochastic Gradient
  MCMC   Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class of
algorithms for scalable Bayesian inference. However, these algorithms include
hyperparameters such as step size or batch size that influence the accuracy of
estimators based on the obtained posterior samples. As a result, these
hyperparameters must be tuned by the practitioner and currently no principled
and automated way to tune them exists. Standard MCMC tuning methods based on
acceptance rates cannot be used for SGMCMC, thus requiring alternative tools
and diagnostics. We propose a novel bandit-based algorithm that tunes the
SGMCMC hyperparameters by minimizing the Stein discrepancy between the true
posterior and its Monte Carlo approximation. We provide theoretical results
supporting this approach and assess various Stein-based discrepancies. We
support our results with experiments on both simulated and real datasets, and
find that this method is practical for a wide range of applications.

8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con