  The acquisition of labels for supervised learning can be expensive. To
improve the sample efficiency of neural network regression, we study active
learning methods that adaptively select batches of unlabeled data for labeling.
We present a framework for constructing such methods out of (network-dependent)
base kernels, kernel transformations, and selection methods. Our framework
encompasses many existing Bayesian methods based on Gaussian process
approximations of neural networks as well as non-Bayesian methods.
Additionally, we propose to replace the commonly used last-layer features with
sketched finite-width neural tangent kernels and to combine them with a novel
clustering method. To evaluate different methods, we introduce an open-source
benchmark consisting of 15 large tabular regression data sets. Our proposed
method outperforms the state-of-the-art on our benchmark, scales to large data
sets, and works out-of-the-box without adjusting the network architecture or
training code. We provide open-source code that includes efficient
implementations of all kernels, kernel transformations, and selection methods,
and can be used for reproducing our results.
