Boosting is a commonly used technique to enhance the performance of a set of
base models by combining them into a strong ensemble model. Though widely
adopted, boosting is typically used in supervised learning where the data is
labeled accurately. However, in weakly supervised learning, where most of the
data is labeled through weak and noisy sources, it remains nontrivial to design
effective boosting approaches. In this work, we show that the standard
implementation of the convex combination of base learners can hardly work due
to the presence of noisy labels. Instead, we propose $\textit{LocalBoost}$, a
novel framework for weakly-supervised boosting. LocalBoost iteratively boosts
the ensemble model from two dimensions, i.e., intra-source and inter-source.
The intra-source boosting introduces locality to the base learners and enables
each base learner to focus on a particular feature regime by training new base
learners on granularity-varying error regions. For the inter-source boosting,
we leverage a conditional function to indicate the weak source where the sample
is more likely to appear. To account for the weak labels, we further design an
estimate-then-modify approach to compute the model weights. Experiments on
seven datasets show that our method significantly outperforms vanilla boosting
methods and other weakly-supervised methods.

本文提出了一种名为 LocalBoost 的弱监督增强框架，从两个维度（即源内和源间）迭代地增强集成模型，进而在七个数据集上达到了优于基准 boosting 方法和其他弱监督方法的表现。