Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations
arXiv:2007.11126
Abstract
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.
Accepted in ICML Workshop on Real World Experiment Design and Active Learning 2020