Efficient Graph-Based Active Learning with Probit Likelihood via\n Gaussian Approximations

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