A hypothesis-testing framework with class-restricted Donsker-Varadhan estimators provides optimal non-asymptotic confidence intervals and minimax lower bounds for black-box auditing of Rényi DP guarantees.
Deep learning with differential privacy
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
AS-LoRA adaptively chooses which LoRA factor to update per layer and round using a curvature-aware second-order score, eliminating reconstruction error floors and improving performance in DP federated learning.
citing papers explorer
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Optimal Guarantees for Auditing R\'enyi Differentially Private Machine Learning
A hypothesis-testing framework with class-restricted Donsker-Varadhan estimators provides optimal non-asymptotic confidence intervals and minimax lower bounds for black-box auditing of Rényi DP guarantees.
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Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning
AS-LoRA adaptively chooses which LoRA factor to update per layer and round using a curvature-aware second-order score, eliminating reconstruction error floors and improving performance in DP federated learning.