Certified Data Removaledit
Concept page for deletion guarantees in machine unlearning.
Certified Data Removal refers to machine-learning methods that provide an explicit guarantee about the effect of removing data from a trained model. In this wiki the concept is used mainly to explain the mathematical side of machine unlearning, where the goal is not only to update a model quickly, but to bound how close the updated model is to a model retrained without the deleted data.1
Role in this wikiedit
This page is a background article for readers who arrive at Hessian-Free Online Certified Unlearning without the unlearning vocabulary. "Certified" does not mean that a model becomes globally safe or fair. It means the method states a measurable deletion criterion, often by comparing parameters, losses, predictions, or distributions before and after removal. That distinction keeps the claim narrower and more testable.
Connection to Qiao's workedit
Qiao's Hessian-free paper is organized around certified deletion under online update constraints. The paper avoids explicit Hessian inversion, which matters because exact second-order operations can be expensive or unstable in deployed systems. Certified data removal therefore connects Qiao's mathematical unlearning work to his broader AI and networks interest: deletion guarantees are valuable only when they can be delivered at realistic computational and latency cost.
See alsoedit
Footnotesedit
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Guo et al., "Certified Data Removal from Machine Learning Models", ICML 2020, is one reference point for treating deletion as a certified approximation to retraining. ↩