Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classification with Costly Features as a Sequential Decision-Making
  Problem   This work focuses on a specific classification problem, where the information
about a sample is not readily available, but has to be acquired for a cost, and
there is a per-sample budget. Inspired by real-world use-cases, we analyze
average and hard variations of a directly specified budget. We postulate the
problem in its explicit formulation and then convert it into an equivalent MDP,
that can be solved with deep reinforcement learning. Also, we evaluate a
real-world inspired setting with sparse training dataset with missing features.
The presented method performs robustly well in all settings across several
distinct datasets, outperforming other prior-art algorithms. The method is
flexible, as showcased with all mentioned modifications and can be improved
with any domain independent advancement in RL.

12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ