LLQR+SAM pairs a slow learned geometry preconditioner with fast SAM perturbations to amplify escape from locally sharp 'potholes' while stabilizing flat basins, producing consistent gains over SAM and LLQR alone.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
HF-KCU approximates influence reversal for unlearning in federated learning using Krylov-subspace conjugate gradients and causal weighting, delivering 47x speedup and privacy restoration on CIFAR-10, MNIST, and Fashion-MNIST while handling bounded adversarial perturbations.
citing papers explorer
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Navigating Potholes with Geometry-Aware Sharpness Minimization
LLQR+SAM pairs a slow learned geometry preconditioner with fast SAM perturbations to amplify escape from locally sharp 'potholes' while stabilizing flat basins, producing consistent gains over SAM and LLQR alone.
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Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions
HF-KCU approximates influence reversal for unlearning in federated learning using Krylov-subspace conjugate gradients and causal weighting, delivering 47x speedup and privacy restoration on CIFAR-10, MNIST, and Fashion-MNIST while handling bounded adversarial perturbations.