Training-Free Neural Active Learning with Initialization-Robustness
  Guarantees
Apivich Hemachandra Zhongxiang Dai Jasraj Singh See-Kiong Ng Bryan Kian Hsiang Low
Existing neural active learning algorithms have aimed to optimize the
predictive performance of neural networks (NNs) by selecting data for
labelling. However, other than a good predictive performance, being robust
against random parameter initializations is also a crucial requirement in
safety-critical applications. To this end, we introduce our expected variance
with Gaussian processes (EV-GP) criterion for neural active learning, which is
theoretically guaranteed to select data points which lead to trained NNs with
both (a) good predictive performances and (b) initialization robustness.
Importantly, our EV-GP criterion is training-free, i.e., it does not require
any training of the NN during data selection, which makes it computationally
efficient. We empirically demonstrate that our EV-GP criterion is highly
correlated with both initialization robustness and generalization performance,
and show that it consistently outperforms baseline methods in terms of both
desiderata, especially in situations with limited initial data or large batch
sizes.