A survey that organizes machine unlearning verification methods into behavioral and parametric categories and outlines open problems.
An introduction to machine unlearning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 2polarities
background 2representative citing papers
Proposes simple techniques and inverse Hessian approximation to enable certified unlearning for nonconvex DNN objectives, including nonconvergent training and sequential unlearning requests.
A survey classifying machine unlearning into centralized (exact and approximate), distributed/irregular data, verification, and privacy/security categories with technique overviews.
citing papers explorer
-
Towards Reliable Forgetting: A Survey on Machine Unlearning Verification
A survey that organizes machine unlearning verification methods into behavioral and parametric categories and outlines open problems.
-
Towards Certified Unlearning for Deep Neural Networks
Proposes simple techniques and inverse Hessian approximation to enable certified unlearning for nonconvex DNN objectives, including nonconvergent training and sequential unlearning requests.
-
Machine Unlearning: A Comprehensive Survey
A survey classifying machine unlearning into centralized (exact and approximate), distributed/irregular data, verification, and privacy/security categories with technique overviews.