Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal Transport for Conditional Domain Matching and Label Shift   We address the problem of unsupervised domain adaptation under the setting of
generalized target shift (joint class-conditional and label shifts). For this
framework, we theoretically show that, for good generalization, it is necessary
to learn a latent representation in which both marginals and class-conditional
distributions are aligned across domains. For this sake, we propose a learning
problem that minimizes importance weighted loss in the source domain and a
Wasserstein distance between weighted marginals. For a proper weighting, we
provide an estimator of target label proportion by blending mixture estimation
and optimal matching by optimal transport. This estimation comes with
theoretical guarantees of correctness under mild assumptions. Our experimental
results show that our method performs better on average than competitors across
a range domain adaptation problems including \emph{digits},\emph{VisDA} and
\emph{Office}. Code for this paper is available at
\url{https://github.com/arakotom/mars_domain_adaptation}.

18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
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The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: astro-ph