Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributionally Robust Local Non-parametric Conditional Estimation   Conditional estimation given specific covariate values (i.e., local
conditional estimation or functional estimation) is ubiquitously useful with
applications in engineering, social and natural sciences. Existing data-driven
non-parametric estimators mostly focus on structured homogeneous data (e.g.,
weakly independent and stationary data), thus they are sensitive to adversarial
noise and may perform poorly under a low sample size. To alleviate these
issues, we propose a new distributionally robust estimator that generates
non-parametric local estimates by minimizing the worst-case conditional
expected loss over all adversarial distributions in a Wasserstein ambiguity
set. We show that despite being generally intractable, the local estimator can
be efficiently found via convex optimization under broadly applicable settings,
and it is robust to the corruption and heterogeneity of the data. Experiments
with synthetic and MNIST datasets show the competitive performance of this new
class of estimators.

7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio