Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal quantisation of probability measures using maximum mean
  discrepancy   Several researchers have proposed minimisation of maximum mean discrepancy
(MMD) as a method to quantise probability measures, i.e., to approximate a
target distribution by a representative point set. We consider sequential
algorithms that greedily minimise MMD over a discrete candidate set. We propose
a novel non-myopic algorithm and, in order to both improve statistical
efficiency and reduce computational cost, we investigate a variant that applies
this technique to a mini-batch of the candidate set at each iteration. When the
candidate points are sampled from the target, the consistency of these new
algorithm - and their mini-batch variants - is established. We demonstrate the
algorithms on a range of important computational problems, including
optimisation of nodes in Bayesian cubature and the thinning of Markov chain
output.

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The categories of this paper are: hep-ph
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an