  We consider the problem of sequentially allocating resources in a censored
semi-bandits setup, where the learner allocates resources at each step to the
arms and observes loss. The loss depends on two hidden parameters, one specific
to the arm but independent of the resource allocation, and the other depends on
the allocated resource. More specifically, the loss equals zero for an arm if
the resource allocated to it exceeds a constant (but unknown) arm dependent
threshold. The goal is to learn a resource allocation that minimizes the
expected loss. The problem is challenging because the loss distribution and
threshold value of each arm are unknown. We study this setting by establishing
its `equivalence' to Multiple-Play Multi-Armed Bandits (MP-MAB) and
Combinatorial Semi-Bandits. Exploiting these equivalences, we derive optimal
algorithms for our problem setting using known algorithms for MP-MAB and
Combinatorial Semi-Bandits. The experiments on synthetically generated data
validate the performance guarantees of the proposed algorithms.
