  Active learning in semi-supervised classification involves introducing
additional labels for unlabelled data to improve the accuracy of the underlying
classifier. A challenge is to identify which points to label to best improve
performance while limiting the number of new labels. "Model-change" active
learning quantifies the resulting change incurred in the classifier by
introducing the additional label(s). We pair this idea with graph-based
semi-supervised learning methods, that use the spectrum of the graph Laplacian
matrix, which can be truncated to avoid prohibitively large computational and
storage costs. We consider a family of convex loss functions for which the
acquisition function can be efficiently approximated using the Laplace
approximation of the posterior distribution. We show a variety of multiclass
examples that illustrate improved performance over prior state-of-art.
