KL divergence between a general distribution and a perturbed Gaussian reference remains stable with an optimal sqrt(ε) degradation rate under finite second-moment conditions.
The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
2 Pith papers cite this work. Polarity classification is still indexing.
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Symmetrizing Bregman divergences on positive definite matrices yields the arithmetic mean as canonical for forward symmetrization and the pulled-back dual arithmetic mean for reverse symmetrization, for any mirror map.
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Optimal Stability of KL Divergence under Gaussian Perturbations
KL divergence between a general distribution and a perturbed Gaussian reference remains stable with an optimal sqrt(ε) degradation rate under finite second-moment conditions.
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Symmetrizing Bregman Divergence on the Cone of Positive Definite Matrices: Which Mean to Use and Why
Symmetrizing Bregman divergences on positive definite matrices yields the arithmetic mean as canonical for forward symmetrization and the pulled-back dual arithmetic mean for reverse symmetrization, for any mirror map.