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

• URL: http://arxiv.org/abs/2407.08169v2
• Date: Thu, 10 Apr 2025 02:33:37 GMT
• Title: The Approximate Fisher Influence Function: Faster Estimation of Data Influence in Statistical Models
• Authors: Omri Lev, Ashia C. Wilson,
• Abstract summary: Quantifying the influence of infinitesimal reform changes in model performance is crucial for understanding and improving machine learning models.<n>We show that our method offers significant computational advantages over current methods.
• Score: 5.893124686141781
• License: http://creativecommons.org/licenses/by/4.0/
• 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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