  Active learning enables efficient model training by leveraging interactions
between machine learning agents and human annotators. We study and propose a
novel framework that formulates batch active learning from the sparse
approximation's perspective. Our active learning method aims to find an
informative subset from the unlabeled data pool such that the corresponding
training loss function approximates its full data pool counterpart. We realize
the framework as sparsity-constrained discontinuous optimization problems,
which explicitly balance uncertainty and representation for large-scale
applications and could be solved by greedy or proximal iterative hard
thresholding algorithms. The proposed method can adapt to various settings,
including both Bayesian and non-Bayesian neural networks. Numerical experiments
show that our work achieves competitive performance across different settings
with lower computational complexity.
