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New MAGE and SPELL algorithms enhance data attribution scalability

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new methods, MAGE and SPELL, to improve the scalability of data attribution in machine learning. These techniques aim to estimate an influence matrix from a limited number of measurements, addressing the computational challenges of existing metagradient-based approaches like MAGIC. The proposed algorithms can be integrated with existing metagradient machinery without additional cost and have demonstrated strong performance across various training scales and measurement budgets in empirical studies. AI

IMPACT These methods could significantly improve the efficiency of understanding how individual data points influence model behavior, aiding in tasks like data valuation and model interpretability.

RANK_REASON The cluster contains a research paper detailing new algorithms for data attribution in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MAGE and SPELL algorithms enhance data attribution scalability

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The cluster contains a research paper detailing new algorithms for data attribution in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 Italiano(IT) · Yuxi Chen, Hamza Golubovic, Han Tong, Arian Maleki, Andrew Ilyas ·

    Data Attribution at Scale via Influence Matrix Estimation

    arXiv:2609.15044v1 Announce Type: cross Abstract: Data attribution seeks to quantify how individual training examples shape a model's predictions and underpins problems including data valuation, machine unlearning, and model interpretability. Despite having a long line of work, c…

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