Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Discovering General-Purpose Active Learning Strategies   We propose a general-purpose approach to discovering active learning (AL)
strategies from data. These strategies are transferable from one domain to
another and can be used in conjunction with many machine learning models. To
this end, we formalize the annotation process as a Markov decision process,
design universal state and action spaces and introduce a new reward function
that precisely model the AL objective of minimizing the annotation cost. We
seek to find an optimal (non-myopic) AL strategy using reinforcement learning.
We evaluate the learned strategies on multiple unrelated domains and show that
they consistently outperform state-of-the-art baselines.

22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph