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

Data Attribution at Scale via Influence Matrix Estimation

Read the original on arXiv Machine Learning →

Data Attribution at Scale via Influence Matrix Estimation proposes a scalable approach to quantify how individual training examples influence a model’s predictions. The authors introduce two algorithms, MAGE and SPELL, that reconstruct an influence matrix from a limited number of measurements without extra computational cost, improving over existing baselines across various training scales and budgets.

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