Geometric Median (GM) Matching for Robust Data Pruning

• URL: http://arxiv.org/abs/2406.17188v1
• Date: Tue, 25 Jun 2024 00:02:01 GMT
• Title: Geometric Median (GM) Matching for Robust Data Pruning
• Authors: Anish Acharya, Inderjit S Dhillon, Sujay Sanghavi,
• Abstract summary: Data pruning is crucial for mitigating the enormous computational costs associated with training data-hungry models at scale. In this work, we propose Geometric ($gm$) Matching that yields a $k$-subset such that the mean of the subset approximates the median of the (potentially) noisy dataset. Experiments across popular deep learning benchmarks indicate that $gm$ Matching consistently outperforms prior state-of-the-art.
• Score: 29.458270105150564
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Unfortunately, the existing heuristics for (robust) data pruning lack theoretical coherence and rely on heroic assumptions, that are, often unattainable, by the very nature of the problem setting. Moreover, these strategies often yield sub-optimal neural scaling laws even compared to random sampling, especially in scenarios involving strong corruption and aggressive pruning rates -- making provably robust data pruning an open challenge. In response, in this work, we propose Geometric Median ($\gm$) Matching -- a herding~\citep{welling2009herding} style greedy algorithm -- that 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 $\gm$ Matching a strong baseline for future research in robust data pruning.

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