When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs
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...
What happened
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...
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1 reports · 1 original report · 1 independent
- Apple Machine Learning ResearchPrimary source · Supports · EN · 100%When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs ↗
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- When Unlearning Is Free: Leveraging Low Influence Points to Reduce Computational Costs Observed
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