ALU Framework: Scalable Unlearning with Better Compute than Retraining
gkdziugaite · x · 2026-07-07
The Asymmetric Langevin Unlearning (ALU) framework boasts two core capabilities: massive unlearning capacity, capable of forgetting a constant fraction of the private dataset (a scale where standard symmetric methods become infeasible); and rigorous computational advantages, as ALU's computational complexity is theoretically superior to retraining from scratch while maintaining unlearning certification.
Related event: ICML 2026 Paper Proposes ALU Framework for Large-Scale Machine Unlearning(5 posts)→
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