Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive joint distribution learning   We develop a new framework for embedding (joint) probability distributions in
tensor product reproducing kernel Hilbert spaces (RKHS). This framework
accommodates a low-dimensional, positive, and normalized model of a
Radon-Nikodym derivative, estimated from sample sizes of up to several million
data points, alleviating the inherent limitations of RKHS modeling.
Well-defined normalized and positive conditional distributions are natural
by-products to our approach. The embedding is fast to compute and naturally
accommodates learning problems ranging from prediction to classification. The
theoretical findings are supplemented by favorable numerical results.

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The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an