Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential
  Families   We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive
MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities
where classical HMC is not an option due to intractable gradients, KMC
adaptively learns the target's gradient structure by fitting an exponential
family model in a Reproducing Kernel Hilbert Space. Computational costs are
reduced by two novel efficient approximations to this gradient. While being
asymptotically exact, KMC mimics HMC in terms of sampling efficiency, and
offers substantial mixing improvements over state-of-the-art gradient free
samplers. We support our claims with experimental studies on both toy and
real-world applications, including Approximate Bayesian Computation and
exact-approximate MCMC.

25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
1
The categories of this paper are: cs
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio