CEAL
Compute-Efficient Active Learning
The motivation of this work is based on the hypothesis that historical values of the acquisition function are good predictors of their future values. This idea is quite intuitive. For example, once a model is certain about its predictions on a given sample, this fact is unlikely to change. This can be explained by the randomness in the training, especially when using small acquisition sizes.