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Found 3 papers, 3 papers with code
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.
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