  Active learning is an important machine learning problem in reducing the
human labeling effort. Current active learning strategies are designed from
human knowledge, and are applied on each dataset in an immutable manner. In
other words, experience about the usefulness of strategies cannot be updated
and transferred to improve active learning on other datasets. This paper
initiates a pioneering study on whether active learning experience can be
transferred. We first propose a novel active learning model that linearly
aggregates existing strategies. The linear weights can then be used to
represent the active learning experience. We equip the model with the popular
linear upper- confidence-bound (LinUCB) algorithm for contextual bandit to
update the weights. Finally, we extend our model to transfer the experience
across datasets with the technique of biased regularization. Empirical studies
demonstrate that the learned experience not only is competitive with existing
strategies on most single datasets, but also can be transferred across datasets
to improve the performance on future learning tasks.
