Papers
1–3 of 3Research Paper·Mar 11, 2026
Unlearning the Unpromptable: Prompt-free Instance Unlearning in Diffusion Models
Machine unlearning aims to remove specific outputs from trained models, often at the concept level, such as forgetting all occurrences of a particular celebrity or filtering content via text prompts. ...
7.0 viability
Research Paper·Jan 26, 2026
FaLW: A Forgetting-aware Loss Reweighting for Long-tailed Unlearning
Machine unlearning, which aims to efficiently remove the influence of specific data from trained models, is crucial for upholding data privacy regulations like the ``right to be forgotten". However, e...
6.0 viability
Research Paper·Mar 11, 2026
Reference-Guided Machine Unlearning
Machine unlearning aims to remove the influence of specific data from trained models while preserving general utility. Existing approximate unlearning methods often rely on performance-degradation heu...
4.0 viability