Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Concentration bounds for linear Monge mapping estimation and optimal
  transport domain adaptation   This article investigates the quality of the estimator of the linear Monge
mapping between distributions. We provide the first concentration result on the
linear mapping operator and prove a sample complexity of $n^{-1/2}$ when using
empirical estimates of first and second order moments. This result is then used
to derive a generalization bound for domain adaptation with optimal transport.
As a consequence, this method approaches the performance of theoretical Bayes
predictor under mild conditions on the covariance structure of the problem. We
also discuss the computational complexity of the linear mapping estimation and
show that when the source and target are stationary the mapping is a
convolution that can be estimated very efficiently using fast Fourier
transforms. Numerical experiments reproduce the behavior of the proven bounds
on simulated and real data for mapping estimation and domain adaptation on
images.

21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc