Hugging Face Trending Papers
Private Online Prediction from Experts with Small Losses
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arXiv Machine Learning
Oct 22
Private Online Prediction from Experts with Small Losses
arXiv:2610. 29575v1 Announce Type: new Abstract: We consider online prediction from experts, a fundamental problem in machine learning, under differential privacy constraints.
arXiv Machine Learning
Oct 2
Certification-Based Differentially Private Learning
The paper extends the abstract gradient training (AGT) framework to provide tighter differential privacy guarantees for both private prediction and private learning. It introduces Abstract Gradient Sampling (AGS) to analyze privacy in continuous, unbounded regression and offers theoretical and empirical evidence that these methods yield tighter bounds than global-sensitivity baselines, even in previously unbounded settings. The authors also demonstrate that their private learning algorithm can outperform standard private learners under comparable conditions.
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 31
Performative Privacy: When Differential Privacy Maximizes Utility
arXiv:2608.28198v1 Announce Type: new Abstract: Privacy-preserving learning is often motivated by the idea that protecting users' data can preserve trust and thus participation, improving utility in...
arXiv Machine Learning
Jul 10
On the Gradient Complexity of Private Optimization with Private Oracles
arXiv:2511. 13999v2 Announce Type: replace Abstract: We study the running time, in terms of first order oracle queries, of differentially private empirical/population risk minimization of Lipschitz convex losses.
arXiv Machine Learning
Aug 11
Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD
arXiv:2601. 10237v3 Announce Type: replace Abstract: Differentially Private Stochastic Gradient Descent (DP-SGD) is the dominant paradigm for private training, but its fundamental limitations under worst-case adversarial privacy definitions remain poorly understood.