aiqbench analysis
This research addresses data privacy in machine learning by optimizing the "unlearning" process, which removes specific training data from models without retraining them entirely. By identifying and ignoring low-influence data points before execution, the proposed framework achieves significant computational savings of up to 50% across language and vision tasks. This efficiency gain is critical for scaling privacy-preserving techniques in resource-constrained environments where full model re-evaluation is prohibitive.
When unlearning is free: leveraging low influence points to reduce computational costs
arXiv:2512.05254v2 Announce Type: replace Abstract: As concerns around data privacy in machine learning grow, the ability to unlearn, or remove, specific data points from trained models becomes increasingly important. While state of the art unlearning methods have emerged in response, they typically treat all points in the forget set equally. In this work, we challenge this approach by asking whether points that have a negligible impact on the model's learning need to be removed. Through a comparative analysis of influence functions across language and vision tasks, we identify subsets of training data with negligible impact on model outputs. Leveraging this insight, we propose an efficient unlearning framework that reduces the size of datasets before unlearning leading to significant computational savings (up to approximately 50 percent) on real world empirical examples.
Abstract:As concerns around data privacy in machine learning grow, the ability to unlearn, or remove, specific data points from trained models becomes increasingly important. While state of the art unlearning methods have emerged in response, they typically treat all points in the forget set equally. In this work, we challenge this approach by asking whether points that have a negligible impact on the model's learning need to be removed. Through a comparative analysis of influence functions across language and vision tasks, we identify subsets of training data with negligible impact on model outputs. Leveraging this insight, we propose an efficient unlearning framework that reduces the size of datasets before unlearning leading to significant computational savings (up to approximately 50 percent) on real world empirical examples.
Machine Learning (cs.LG)
arXiv:2512.05254 [cs.LG]
(or arXiv:2512.05254v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2512.05254 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 217 words
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