  As an important branch of weakly supervised learning, partial label learning
deals with data where each instance is assigned with a set of candidate labels,
whereas only one of them is true. Despite many methodology studies on learning
from partial labels, there still lacks theoretical understandings of their risk
consistent properties under relatively weak assumptions, especially on the link
between theoretical results and the empirical choice of parameters. In this
paper, we propose a family of loss functions named \textit{Leveraged Weighted}
(LW) loss, which for the first time introduces the leverage parameter $\beta$
to consider the trade-off between losses on partial labels and non-partial
ones. From the theoretical side, we derive a generalized result of risk
consistency for the LW loss in learning from partial labels, based on which we
provide guidance to the choice of the leverage parameter $\beta$. In
experiments, we verify the theoretical guidance, and show the high
effectiveness of our proposed LW loss on both benchmark and real datasets
compared with other state-of-the-art partial label learning algorithms.
