  We present a novel adaptation of active learning to graph-based
semi-supervised learning (SSL) under non-Gaussian Bayesian models. We present
an approximation of non-Gaussian distributions to adapt previously
Gaussian-based acquisition functions to these more general cases. We develop an
efficient rank-one update for applying "look-ahead" based methods as well as
model retraining. We also introduce a novel "model change" acquisition function
based on these approximations that further expands the available collection of
active learning acquisition functions for such methods.
