2025/12/04 by Anat Kleiman, Kleiman, Anat, Robert Fisher +5
Computer Science · #Adversarial Robustness in Machine Learning #Domain Adaptation and Few-Shot Learning #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.LG
paper · pdf · doi:10.48550/arxiv.2512.05254
openalex publication_date 2025/12/04 · openalex created_date 2025/12/09 · openalex updated_date 2026/07/28 · arxiv created 2026/07/29 · arxiv updated 2026/07/31
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.