  Active learning aims to reduce labeling efforts by selectively asking humans
to annotate the most important data points from an unlabeled pool and is an
example of human-machine interaction. Though active learning has been
extensively researched for classification and ranking problems, it is
relatively understudied for regression problems. Most existing active learning
for regression methods use the regression function learned at each active
learning iteration to select the next informative point to query. This
introduces several challenges such as handling noisy labels, parameter
uncertainty and overcoming initially biased training data. Instead, we propose
a feature-focused approach that formulates both sequential and batch-mode
active regression as a novel bipartite graph optimization problem. We conduct
experiments on both noise-free and noisy settings. Our experimental results on
benchmark data sets demonstrate the effectiveness of our proposed approach.
