  Semi-supervised learning (SSL) algorithms have had great success in recent
years in limited labeled data regimes. However, the current state-of-the-art
SSL algorithms are computationally expensive and entail significant compute
time and energy requirements. This can prove to be a huge limitation for many
smaller companies and academic groups. Our main insight is that training on a
subset of unlabeled data instead of entire unlabeled data enables the current
SSL algorithms to converge faster, significantly reducing computational costs.
In this work, we propose RETRIEVE, a coreset selection framework for efficient
and robust semi-supervised learning. RETRIEVE selects the coreset by solving a
mixed discrete-continuous bi-level optimization problem such that the selected
coreset minimizes the labeled set loss. We use a one-step gradient
approximation and show that the discrete optimization problem is approximately
submodular, enabling simple greedy algorithms to obtain the coreset. We
empirically demonstrate on several real-world datasets that existing SSL
algorithms like VAT, Mean-Teacher, FixMatch, when used with RETRIEVE, achieve
a) faster training times, b) better performance when unlabeled data consists of
Out-of-Distribution (OOD) data and imbalance. More specifically, we show that
with minimal accuracy degradation, RETRIEVE achieves a speedup of around
$3\times$ in the traditional SSL setting and achieves a speedup of $5\times$
compared to state-of-the-art (SOTA) robust SSL algorithms in the case of
imbalance and OOD data. RETRIEVE is available as a part of the CORDS toolkit:
https://github.com/decile-team/cords.
