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cs.LG2026
Spectral Saliency for Machine Unlearning
Cedar Site Bai, Amber Yijia Zheng, Raymond A. Yeh +1
Machine unlearning (MU) aims to remove the influence of specific training data while preserving model utility. As the name suggests, MU can be viewed as the inverse of learning, us…
cs.LG2025
Knowledge Distillation Detection for Open-weights Models
Qin Shi, Amber Yijia Zheng, Qifan Song +1
We propose the task of knowledge distillation detection, which aims to determine whether a student model has been distilled from a given teacher, under a practical setting where on…
cs.LG2025
Model Immunization from a Condition Number Perspective
Amber Yijia Zheng, Cedar Site Bai, Brian Bullins +1
Model immunization aims to pre-train models that are difficult to fine-tune on harmful tasks while retaining their utility on other non-harmful tasks. Though prior work has shown e…