18 citations · 18 across the 3 of their papers we have counts for
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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 training data from a model while preserving its general utility. In vision, many approximate unlearning methods pursue t…
cs.LG2024★ 18 cited
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…