Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bandit Labor Training   On-demand labor platforms aim to train a skilled workforce to serve its
incoming demand for jobs. Since limited jobs are available for training, and it
is usually not necessary to train all workers, efficient matching of training
jobs requires prioritizing fast learners over slow ones. However, the learning
rates of novice workers are unknown, resulting in a tradeoff between
exploration (learning the learning rates) and exploitation (training the best
workers). Motivated to study this tradeoff, we analyze a novel objective within
the stochastic multi-armed bandit framework. Given $K$ arms, instead of
maximizing the expected total reward from $T$ pulls (the traditional "sum"
objective), we consider the vector of cumulative rewards earned from the $K$
arms at the end of $T$ pulls and aim to maximize the expected highest
cumulative reward (the "max" objective). When rewards represent skill
increments, this corresponds to the objective of training a single highly
skilled worker from a set of novice workers, using a limited supply of training
jobs. For this objective, we show that any policy must incur an
instance-dependent asymptotic regret of $\Omega(\log T)$ (with a higher
instance-dependent constant) and a worst-case regret of
$\Omega(K^{1/3}T^{2/3})$. We then design an explore-then-commit policy
featuring exploration based on appropriately tuned confidence bounds on the
mean reward and an adaptive stopping criterion, which adapts to the problem
difficulty and achieves these bounds (up to logarithmic factors). We generalize
our algorithmic insights to the problem of maximizing the expected value of the
average cumulative reward of the top $m$ arms with the highest cumulative
rewards, corresponding to the case where multiple workers must be trained. Our
numerical experiments demonstrate the efficacy of our policies compared to
several natural alternatives in practical parameter regimes.

31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat