Neural networks trained on scalar diffusion data predict adaptive coarse basis functions for Schwarz methods, transferring without retraining to linear elasticity and nonlinear p-Laplace problems.
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Jacobian-guided reshaping converts isotropic LDP noise into an anisotropic distribution focused on task-relevant subspaces, yielding roughly 20% utility gains on CIFAR-10-C for PrivUnit2 and PrivUnitG at ε=7.5 while keeping per-dimension privacy budgets uniform.
citing papers explorer
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Learning Adaptive Coarse Spaces Using Transferable Neural Network Models for Linear and Nonlinear Overlapping Domain Decomposition Methods
Neural networks trained on scalar diffusion data predict adaptive coarse basis functions for Schwarz methods, transferring without retraining to linear elasticity and nonlinear p-Laplace problems.
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Jacobian-Guided Anisotropic Noise Reshaping for Enhancing Representation Utility under Local Differential Privacy
Jacobian-guided reshaping converts isotropic LDP noise into an anisotropic distribution focused on task-relevant subspaces, yielding roughly 20% utility gains on CIFAR-10-C for PrivUnit2 and PrivUnitG at ε=7.5 while keeping per-dimension privacy budgets uniform.