DP-NGD enables second-order optimization under differential privacy by decoupling curvature estimation onto public data, performing isotropic DP operations in a whitened space, and dynamically clamping curvature eigenvalues to prevent instability.
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Differentially Private Natural Gradient Descent
DP-NGD enables second-order optimization under differential privacy by decoupling curvature estimation onto public data, performing isotropic DP operations in a whitened space, and dynamically clamping curvature eigenvalues to prevent instability.