Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rapidly Personalizing Mobile Health Treatment Policies with Limited Data   In mobile health (mHealth), reinforcement learning algorithms that adapt to
one's context without learning personalized policies might fail to distinguish
between the needs of individuals. Yet the high amount of noise due to the in
situ delivery of mHealth interventions can cripple the ability of an algorithm
to learn when given access to only a single user's data, making personalization
challenging. We present IntelligentPooling, which learns personalized policies
via an adaptive, principled use of other users' data. We show that
IntelligentPooling achieves an average of 26% lower regret than
state-of-the-art across all generative models. Additionally, we inspect the
behavior of this approach in a live clinical trial, demonstrating its ability
to learn from even a small group of users.

37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat