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Found 4 papers, 3 papers with code
What makes unlearning hard and what to do about it
Based on our insights, we develop a framework coined Refined-Unlearning Meta-algorithm (RUM) that encompasses: (i) refining the forget set into homogenized subsets, according to different characteristics; and (ii) a meta-algorithm that employs existing algorithms to unlearn each subset and finally delivers a model that has unlearned the overall forget set.
Detect, Distill and Update: Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data
One open problem in this setting is how to update such ML models in the presence of data updates.
Towards Unbounded Machine Unlearning
This paper is the first, to our knowledge, to study unlearning for different applications (RB, RC, UP), with the view that each has its own desiderata, definitions for `forgetting' and associated metrics for forget quality.
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data
One open problem in this setting is how to update such ML models in the presence of data updates.
