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arXiv · 2407.08169

The Approximate Fisher Influence Function: Faster Estimation of Data Influence in Statistical Models

Omri Lev·Ashia C. Wilson
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Abstract

Quantifying the influence of infinitesimal changes in training data on model performance is crucial for understanding and improving machine learning models. In this work, we reformulate this problem as a weighted empirical risk minimization and enhance existing influence function-based methods by using information geometry to derive a new algorithm to estimate influence. Our formulation proves versatile across various applications, and we further demonstrate in simulations how it remains informative even in non-convex cases. Furthermore, we show that our method offers significant computational advantages over current Newton step-based methods.

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Omri Lev, Ashia C. Wilson. 2024-07-11. The Approximate Fisher Influence Function: Faster Estimation of Data Influence in Statistical Models. https://arxiv.org/abs/2407.08169

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