DP-IVON-Gradsq: Differentially Private Squared-Gradient Improved Variational Online Newton
Differential privacy provides formal privacy guarantees for training neural networks on sensitive data, while Bayesian deep learning offers a principled framework for uncertainty-aware prediction. Combining these two objectives remains challenging, as privacy noise can interact with the stochasticity introduced by Bayesian posterior sampling. In this work, we investigate differentially private variational Bayesian learning through the Improved Variational Online Newton (IVON) optimizer. We intro
Record details
Published: 26 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Privacy · Finance, VC & PE
Retrieved: 29 July 2026
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How to cite this record
ethics.ai (26 July 2026), “DP-IVON-Gradsq: Differentially Private Squared-Gradient Improved Variational Online Newton,” evidence record 14494, https://ethics.ai/record/14494 (originally published by arXiv cs.LG).
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