MAGIC: Near-Optimal Data Attribution for Deep Learning

Andrew Ilyas; Logan Engstrom

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arXiv · 2504.16430

MAGIC: Near-Optimal Data Attribution for Deep Learning

Andrew Ilyas·Logan Engstrom

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Abstract

The goal of predictive data attribution is to estimate how adding or removing a given set of training datapoints will affect model predictions. In convex settings, this goal is straightforward (i.e., via the infinitesimal jackknife). In large-scale (non-convex) settings, however, existing methods are far less successful -- current methods' estimates often only weakly correlate with ground truth. In this work, we present a new data attribution method (MAGIC) that combines classical methods and recent advances in metadifferentiation to (nearly) optimally estimate the effect of adding or removing training data on model predictions.

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Andrew Ilyas, Logan Engstrom. 2025-04-23. MAGIC: Near-Optimal Data Attribution for Deep Learning. https://arxiv.org/abs/2504.16430

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