The paper decomposes errors in trajectory-based data attribution into config, algorithm, and system levels, proposes AdamW-influence to fix optimizer mismatch, derives an error proxy for Taylor approximation, and unifies data selection under a K-step look-ahead framework.
MAGIC: Near-optimal data attribution for deep learning.CoRR, abs/2504.16430
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Kernel surrogate models with first-order gradient approximation achieve 25% higher correlation to leave-one-out ground truth for task attribution and 40% better downstream data selection than linear surrogates.
A sketching method based on higher-order derivatives enables efficient data deletion predictions for deep learning models under a stability assumption with near-linear overhead in error and failure parameters.
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How Faithful Is Trajectory-Based Data Attribution? Error Sources, Remedies, and Practical Guidelines
The paper decomposes errors in trajectory-based data attribution into config, algorithm, and system levels, proposes AdamW-influence to fix optimizer mismatch, derives an error proxy for Taylor approximation, and unifies data selection under a K-step look-ahead framework.
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