Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork
DGX agentarXiv:2606.26772v1 Announce Type: new Abstract: Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD