arXiv Machine Learning
Locally Private Inference for Riemannian Stochastic Optimization
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jul 28
DP-IVON-Gradsq: Differentially Private Squared-Gradient Improved Variational Online Newton
arXiv:2607. 23649v1 Announce Type: new Abstract: 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.
arXiv Machine Learning
Aug 3
Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering
arXiv:2607. 29675v1 Announce Type: cross Abstract: Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy constraints remains largely unexplored.
arXiv Machine Learning
Aug 24
Federated and differentially private estimation of KL divergence
arXiv:2411.16478v3 Announce Type: replace Abstract: Measuring distribution drifts is a key task in managing distributed, sensitive data, as it underpins a wide range of federated learning and analyti...
arXiv Machine Learning
Jul 1
Private Rate-Constrained Optimization with Applications to Fair Learning
arXiv:2505. 22703v2 Announce Type: replace Abstract: Many problems in trustworthy ML can be expressed as constraints on prediction rates across subpopulations, including group fairness constraints (demographic parity, equalized odds, etc.
arXiv Machine Learning
Aug 18
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning
arXiv:2303. 07152v3 Announce Type: replace-cross Abstract: Achieving optimal statistical performance while ensuring the privacy of personal data is a challenging yet crucial objective in modern data analysis.
arXiv Machine Learning
Jul 31
Quadratic Objective Perturbation: Curvature-Based Differential Privacy
arXiv:2605. 05905v2 Announce Type: replace Abstract: Objective perturbation is a standard mechanism in differentially private empirical risk minimization.