BID-LoRA uses bi-directional low-rank adapters with retain/new/unlearn pathways and escape unlearning to enable continual learning and unlearning while minimizing knowledge leakage and parameter updates.
Label- only membership inference attacks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Advanced generalization techniques such as augmentation and early stopping can reduce membership inference attack success rates by up to 100 times, confirmed across more than 1,000 models.
citing papers explorer
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BID-LoRA: A Parameter-Efficient Framework for Continual Learning and Unlearning
BID-LoRA uses bi-directional low-rank adapters with retain/new/unlearn pathways and escape unlearning to enable continual learning and unlearning while minimizing knowledge leakage and parameter updates.
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Generalization and Membership Inference Attack a Practical Perspective
Advanced generalization techniques such as augmentation and early stopping can reduce membership inference attack success rates by up to 100 times, confirmed across more than 1,000 models.