Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Kernel Bayesian Inference with Posterior Regularization   We propose a vector-valued regression problem whose solution is equivalent to
the reproducing kernel Hilbert space (RKHS) embedding of the Bayesian posterior
distribution. This equivalence provides a new understanding of kernel Bayesian
inference. Moreover, the optimization problem induces a new regularization for
the posterior embedding estimator, which is faster and has comparable
performance to the squared regularization in kernel Bayes' rule. This
regularization coincides with a former thresholding approach used in kernel
POMDPs whose consistency remains to be established. Our theoretical work solves
this open problem and provides consistency analysis in regression settings.
Based on our optimizational formulation, we propose a flexible Bayesian
posterior regularization framework which for the first time enables us to put
regularization at the distribution level. We apply this method to nonparametric
state-space filtering tasks with extremely nonlinear dynamics and show
performance gains over all other baselines.

24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph