Summary of Hyperinf: Unleashing the Hyperpower Of the Schulz’s Method For Data Influence Estimation, by Xinyu Zhou et al.
HyperINF: Unleashing the HyperPower of the Schulz’s Method for Data Influence Estimation
by Xinyu Zhou, Simin Fan, Martin Jaggi
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper introduces HyperINF, a novel influence function approximation method that leverages Schulz’s iterative algorithm from the hyperpower family of methods. This technique addresses the limitation of high computational costs associated with influence functions on large-scale models and datasets by providing accurate estimation while minimizing memory and computation requirements. By applying rigorous convergence guarantees from the hyperpower method, HyperINF aims to overcome the limitations of existing approximation methods that often suffer from inaccurate estimation due to lack of strong convergence guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to measure how individual training samples affect a model’s performance on a specific target task. This is important because it can help us understand why some models work better than others and improve their accuracy. The current method for doing this, called influence functions, is too slow for big models and datasets. Researchers have tried to speed up the process by approximating influence functions, but these methods often don’t give very accurate results. This paper introduces a new approach that uses an algorithm from the hyperpower family of methods to efficiently approximate influence functions while ensuring they are accurate. |
