Learning from Label Proportions by Learning with Label Noise

Understand

Learning from label proportions (LLP) is a weakly supervised classification problem where data points are grouped into bags, and the label proportions within each bag are observed instead of the instance-level labels.

• The task is to learn a classifier to predict the individual labels of future individual instances.
• Prior work on LLP for multi-class data has yet to develop a theoretically grounded algorithm.
• In this work, we provide a theoretically grounded approach to LLP based on a reduction to learning with label noise, using the forward correction (FC) loss of \citet{Patrini2017MakingDN}.