Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-Stationary Delayed Bandits with Intermediate Observations   Online recommender systems often face long delays in receiving feedback,
especially when optimizing for some long-term metrics. While mitigating the
effects of delays in learning is well-understood in stationary environments,
the problem becomes much more challenging when the environment changes. In
fact, if the timescale of the change is comparable to the delay, it is
impossible to learn about the environment, since the available observations are
already obsolete. However, the arising issues can be addressed if intermediate
signals are available without delay, such that given those signals, the
long-term behavior of the system is stationary. To model this situation, we
introduce the problem of stochastic, non-stationary, delayed bandits with
intermediate observations. We develop a computationally efficient algorithm
based on UCRL, and prove sublinear regret guarantees for its performance.
Experimental results demonstrate that our method is able to learn in
non-stationary delayed environments where existing methods fail.

21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
10
The categories of this paper are: q-bio
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga