  In this work, we initiate the study of one-round active learning, which aims
to select a subset of unlabeled data points that achieve the highest model
performance after being labeled with only the information from initially
labeled data points. The challenge of directly applying existing data selection
criteria to the one-round setting is that they are not indicative of model
performance when available labeled data is limited. We address the challenge by
explicitly modeling the dependence of model performance on the dataset.
Specifically, we propose DULO, a data-driven framework for one-round active
learning, wherein we learn a model to predict the model performance for a given
dataset and then leverage this model to guide the selection of unlabeled data.
Our results demonstrate that DULO leads to the state-of-the-art performance on
various active learning benchmarks in the one-round setting.
