  We propose a new method for approximating active learning acquisition
strategies that are based on retraining with hypothetically-labeled candidate
data points. Although this is usually infeasible with deep networks, we use the
neural tangent kernel to approximate the result of retraining, and prove that
this approximation works asymptotically even in an active learning setup --
approximating "look-ahead" selection criteria with far less computation
required. This also enables us to conduct sequential active learning, i.e.
updating the model in a streaming regime, without needing to retrain the model
with SGD after adding each new data point. Moreover, our querying strategy,
which better understands how the model's predictions will change by adding new
data points in comparison to the standard ("myopic") criteria, beats other
look-ahead strategies by large margins, and achieves equal or better
performance compared to state-of-the-art methods on several benchmark datasets
in pool-based active learning.
