Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-driven Optimal Cost Selection for Distributionally Robust
  Optimization   Recently, (Blanchet, Kang, and Murhy 2016, and Blanchet, and Kang 2017)
showed that several machine learning algorithms, such as square-root Lasso,
Support Vector Machines, and regularized logistic regression, among many
others, can be represented exactly as distributionally robust optimization
(DRO) problems. The distributional uncertainty is defined as a neighborhood
centered at the empirical distribution. We propose a methodology which learns
such neighborhood in a natural data-driven way. We show rigorously that our
framework encompasses adaptive regularization as a particular case. Moreover,
we demonstrate empirically that our proposed methodology is able to improve
upon a wide range of popular machine learning estimators.

27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
6
The categories of this paper are: astro-ph
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
19
The categories of this paper are: eess
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
1
The categories of this paper are: cs