The first study of unlearning in offline stochastic multi-armed bandits formalizes privacy constraints and delivers adaptive algorithms with performance guarantees and lower bounds for single- and multi-source scenarios under fixed-sample and distribution models.
arXiv preprint arXiv:2208.06875 , year=
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CURE disentangles LLM recommendation circuits into forget-specific, retain-specific, and task-shared modules with tailored update rules to achieve more effective unlearning than weighted baselines.
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Unlearning Offline Stochastic Multi-Armed Bandits
The first study of unlearning in offline stochastic multi-armed bandits formalizes privacy constraints and delivers adaptive algorithms with performance guarantees and lower bounds for single- and multi-source scenarios under fixed-sample and distribution models.
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CURE:Circuit-Aware Unlearning for LLM-based Recommendation
CURE disentangles LLM recommendation circuits into forget-specific, retain-specific, and task-shared modules with tailored update rules to achieve more effective unlearning than weighted baselines.