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Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
In this work, we propose a novel method -- Convex-Concave Loss, which enables a high variance of training loss distribution by gradient descent.
Exploring Learning Complexity for Downstream Data Pruning
In this paper, we propose to treat the learning complexity (LC) as the scoring function for classification and regression tasks.
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