  Data subset selection aims to find a smaller yet informative subset of a
large dataset that can approximate the full-dataset training, addressing
challenges associated with training neural networks on large-scale datasets.
However, existing methods tend to specialize in either high or low selection
ratio regimes, lacking a universal approach that consistently achieves
competitive performance across a broad range of selection ratios. We introduce
a universal and efficient data subset selection method, Best Window Selection
(BWS), by proposing a method to choose the best window subset from samples
ordered based on their difficulty scores. This approach offers flexibility by
allowing the choice of window intervals that span from easy to difficult
samples. Furthermore, we provide an efficient mechanism for selecting the best
window subset by evaluating its quality using kernel ridge regression. Our
experimental results demonstrate the superior performance of BWS compared to
other baselines across a broad range of selection ratios over datasets,
including CIFAR-10/100 and ImageNet, and the scenarios involving training from
random initialization or fine-tuning of pre-trained models.
