Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Combining Machine Learning with Decision Making   We present a new application and covering number bound for the framework of
"Machine Learning with Operational Costs (MLOC)," which is an exploratory form
of decision theory. The MLOC framework incorporates knowledge about how a
predictive model will be used for a subsequent task, thus combining machine
learning with the decision that is made afterwards. In this work, we use the
MLOC framework to study a problem that has implications for power grid
reliability and maintenance, called the Machine Learning and Traveling
Repairman Problem ML&TRP. The goal of the ML&TRP is to determine a route for a
"repair crew," which repairs nodes on a graph. The repair crew aims to minimize
the cost of failures at the nodes, but as in many real situations, the failure
probabilities are not known and must be estimated. The MLOC framework allows us
to understand how this uncertainty influences the repair route. We also present
new covering number generalization bounds for the MLOC framework.

30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg