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cs.LG2026
Reference-Guided Machine Unlearning
Jonas Mirlach, Sonia Laguna, Julia E. Vogt
Machine unlearning aims to remove the influence of specific data from trained models while preserving general utility. Existing approximate unlearning methods often rely on perform…
cs.LG2025
On the Challenges and Opportunities in Generative AI
Laura Manduchi, Clara Meister, Kushagra Pandey +23
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervi…