Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semi-Supervised Single- and Multi-Domain Regression with Multi-Domain
  Training   We address the problems of multi-domain and single-domain regression based on
distinct and unpaired labeled training sets for each of the domains and a large
unlabeled training set from all domains. We formulate these problems as a
Bayesian estimation with partial knowledge of statistical relations. We propose
a worst-case design strategy and study the resulting estimators. Our analysis
explicitly accounts for the cardinality of the labeled sets and includes the
special cases in which one of the labeled sets is very large or, in the other
extreme, completely missing. We demonstrate our estimators in the context of
removing expressions from facial images and in the context of audio-visual word
recognition, and provide comparisons to several recently proposed multi-modal
learning algorithms.

25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th