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cs.LG2026
The Unseen Threat: Residual Knowledge in Machine Unlearning under Perturbed Samples
Hsiang Hsu, Pradeep Niroula, Zichang He +3
Machine unlearning offers a practical alternative to avoid full model re-training by approximately removing the influence of specific user data. While existing methods certify unle…
cs.LG2024
The Effect of Data Poisoning on Counterfactual Explanations
André Artelt, Shubham Sharma, Freddy Lecué +1
Counterfactual explanations are a widely used approach for examining the predictions of black-box systems. They can offer the opportunity for computational recourse by suggesting a…