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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GPP trains local variational encoders in federated settings to release representations that keep utility within 1% of an autoencoder baseline while driving adversary AUC on sensitive attributes to near-random levels on MNIST, CelebA, and HAPT data.
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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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Distributed Deep Variational Approach for Privacy-preserving Data Release
GPP trains local variational encoders in federated settings to release representations that keep utility within 1% of an autoencoder baseline while driving adversary AUC on sensitive attributes to near-random levels on MNIST, CelebA, and HAPT data.