  Active learning (AL) for multiple target models aims to reduce labeled data
querying while effectively training multiple models concurrently. Existing AL
algorithms often rely on iterative model training, which can be computationally
expensive, particularly for deep models. In this paper, we propose a one-shot
AL method to address this challenge, which performs all label queries without
repeated model training.
  Specifically, we extract different representations of the same dataset using
distinct network backbones, and actively learn the linear prediction layer on
each representation via an $\ell_p$-regression formulation. The regression
problems are solved approximately by sampling and reweighting the unlabeled
instances based on their maximum Lewis weights across the representations. An
upper bound on the number of samples needed is provided with a rigorous
analysis for $p\in [1, +\infty)$.
  Experimental results on 11 benchmarks show that our one-shot approach
achieves competitive performances with the state-of-the-art AL methods for
multiple target models.
