Geometric Median (GM) Matching for Robust Data Pruning

ICLR 2025 Conference Submission1402 Authors

Keywords: data pruning, robust, data selection

TL;DR: We propose Geometric Median ( GM) Matching – a novel data pruning strategy that remains robust even when up to 1/2 fraction of the data is arbitrarily corrupted.

Abstract:

Data pruning, the combinatorial task of selecting a small and informative subset from a large dataset, is crucial for mitigating the enormous computational costs associated with training data-hungry modern deep learning models at scale. Since large-scale data collections are invariably noisy, developing data pruning strategies that remain robust even in the presence of corruption is critical in practice.
In response, we propose $\gmm$ -- a herding~\citep{welling2009herding} style greedy algorithm -- that {\em yields a $k$-subset such that the mean of the subset approximates the geometric median of the (potentially) noisy dataset}. Theoretically, we show that $\gm$ Matching enjoys an improved $\gO(1/k)$ scaling over $\gO(1/\sqrt{k})$ scaling of uniform sampling; while achieving the optimal breakdown point of 1/2 even under arbitrary corruption. Extensive experiments across popular deep learning benchmarks indicate that $\gm$ Matching consistently outperforms prior state-of-the-art; the gains become more profound at high rates of corruption and aggressive pruning rates; making it a strong baseline for robust data pruning.

Primary Area: datasets and benchmarks

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Submission Number: 1402