A hypothesis testing approach to distributional unlearning that characterizes allowable edited distributions and removal-preservation Pareto frontiers for parametric and nonparametric families including Gaussians, Poisson, and Gaussian white noise.
Thenε 1 ≤ε 2 if and only if fX,ε1(x)≥f X,ε2(x)for allx∈[0,1].(27) Intuition and importance:The proof of this lemma is along the same lines of the proof given in Dong et al
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Statistical Unlearning of Distributions: A Hypothesis Testing Approach
A hypothesis testing approach to distributional unlearning that characterizes allowable edited distributions and removal-preservation Pareto frontiers for parametric and nonparametric families including Gaussians, Poisson, and Gaussian white noise.