RESEARCHMonitorWATCHLIST
The Utility and Complexity of in- and out-of-Distribution Machine Unlearning
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
A new paper analyzes theoretical trade-offs in machine unlearning to provide rigorous complexity bounds for data removal in AI models.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 8 October 2026
- Collected by OneBench
- 9 Oct 2026, 03:02 UK
Stored source excerpt
arXiv:2412.09119v4 Announce Type: replace Abstract: Machine unlearning, the process of selectively removing data from trained models, is increasingly crucial for addressing privacy concerns and knowledge…
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