Corrective machine unlearning
Machine Learning models increasingly face data integrity challenges due to the use of large-scale training datasets drawn from the internet. We study what model developers can do if they detect that some data was manipulated or incorrect. Such manipulated data can cause adverse effects like vulnerab...
| Hlavní autoři: | , , , , |
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| Médium: | Conference item |
| Jazyk: | English |
| Vydáno: |
OpenReview
2024
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