  \textit{Multiple Instance Learning} (MIL) is concerned with learning from
bags of instances, where only bag labels are given and instance labels are
unknown. Existent approaches in this field were mainly designed for the
bag-level label prediction (predict labels for bags) but not the instance-level
(predict labels for instances), with the task loss being only defined at the
bag level. This restricts their application in many tasks, where the
instance-level labels are more interested. In this paper, we propose a novel
algorithm, whose loss is specifically defined at the instance level, to address
instance-level label prediction in MIL. We prove that the loss of this
algorithm can be unbiasedly and consistently estimated without using instance
labels, under the i.i.d assumption. Empirical study validates the above
statements and shows that the proposed algorithm can achieve superior
instance-level and comparative bag-level performance, compared to
state-of-the-art MIL methods. In addition, it shows that the proposed method
can achieve similar results as the fully supervised model (trained with
instance labels) for label prediction at the instance level.
