Robust Data Pruning: Uncovering and Overcoming Implicit Bias
Artem Vysogorets Kartik Ahuja Julia Kempe
In the era of exceptionally data-hungry models, careful selection of the
training data is essential to mitigate the extensive costs of deep learning.
Data pruning offers a solution by removing redundant or uninformative samples
from the dataset, which yields faster convergence and improved neural scaling
laws. However, little is known about its impact on classification bias of the
trained models. We conduct the first systematic study of this effect and reveal
that existing data pruning algorithms can produce highly biased classifiers. At
the same time, we argue that random data pruning with appropriate class ratios
has potential to improve the worst-class performance. We propose a
"fairness-aware" approach to pruning and empirically demonstrate its
performance on standard computer vision benchmarks. In sharp contrast to
existing algorithms, our proposed method continues improving robustness at a
tolerable drop of average performance as we prune more from the datasets. We
present theoretical analysis of the classification risk in a mixture of
Gaussians to further motivate our algorithm and support our findings.