Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural
  Transport   Hamiltonian Monte Carlo is a powerful algorithm for sampling from
difficult-to-normalize posterior distributions. However, when the geometry of
the posterior is unfavorable, it may take many expensive evaluations of the
target distribution and its gradient to converge and mix. We propose neural
transport (NeuTra) HMC, a technique for learning to correct this sort of
unfavorable geometry using inverse autoregressive flows (IAF), a powerful
neural variational inference technique. The IAF is trained to minimize the KL
divergence from an isotropic Gaussian to the warped posterior, and then HMC
sampling is performed in the warped space. We evaluate NeuTra HMC on a variety
of synthetic and real problems, and find that it significantly outperforms
vanilla HMC both in time to reach the stationary distribution and asymptotic
effective-sample-size rates.

18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an