Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Semi-Supervised Learning in Contextual Bandits with Episodic
  Reward   We considered a novel practical problem of online learning with episodically
revealed rewards, motivated by several real-world applications, where the
contexts are nonstationary over different episodes and the reward feedbacks are
not always available to the decision making agents. For this online
semi-supervised learning setting, we introduced Background Episodic Reward
LinUCB (BerlinUCB), a solution that easily incorporates clustering as a
self-supervision module to provide useful side information when rewards are not
observed. Our experiments on a variety of datasets, both in stationary and
nonstationary environments of six different scenarios, demonstrated clear
advantages of the proposed approach over the standard contextual bandit.
Lastly, we introduced a relevant real-life example where this problem setting
is especially useful.

9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph