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arXiv Machine Learning By Yuxi Chen, Hamza Golubovic, Han Tong, Arian Maleki, Andrew Ilyas

Data Attribution via Sketched Metadifferentiation

Read the original on arXiv Machine Learning →

The paper introduces two algorithms, MAGE and SPELL, that enable efficient data attribution in neural networks by estimating a large influence matrix from a limited number of measurements. These methods leverage existing metagradient techniques without additional computational overhead, addressing the challenge of predicting the impact of removing training data in non‑convex models. Experiments show that MAGE and SPELL outperform current baselines across various training scales and measurement budgets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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