Second-order optimizers retain residual geometric memory in their state after unlearning that first-order metrics miss, and only controlled eigendecay perturbations fully erase it.
Thealgorithmicfoundationsofdifferentialprivacy.Foundations and Trends®in Theoretical Computer Science, 9(3–4):211–487, August 2014
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Shape of Memory: a Geometric Analysis of Machine Unlearning in Second-Order Optimizers
Second-order optimizers retain residual geometric memory in their state after unlearning that first-order metrics miss, and only controlled eigendecay perturbations fully erase it.