  We scale perceived distances of the core-set algorithm by a factor of
uncertainty and search for low-confidence configurations, finding significant
improvements in sample efficiency across CIFAR10/100 and SVHN image
classification, especially in larger acquisition sizes. We show the necessity
of our modifications and explain how the improvement is due to a probabilistic
quadratic speed-up in the convergence of core-set loss, under assumptions about
the relationship of model uncertainty and misclassification.
