Weidong Zheng
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This paper analyzes and proposes four novel attack methods—based on model parameters and model inversion—to demonstrate that existing machine unlearning techniques can inadvertently leak the categories of the forgotten data.
This paper diagnoses a bias-dominated shortcut in class-level machine unlearning, where forgetting is achieved by suppressing classification head biases, and proposes bias-aware mechanisms to mitigate this issue.
Papers
Classification-Head Bias in Class-Level Machine Unlearning: Diagnosis, Mitigation, and Evaluation
This paper diagnoses a bias-dominated shortcut in class-level machine unlearning, where forgetting is achieved by suppressing classification head biases, and proposes bias-aware mechanisms to mitigate…