arXiv:2310. 19043v3 Announce Type: replace-cross Abstract: Recent years have witnessed growing concerns about the privacy of sensitive data.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2503. 18721v3 Announce Type: replace-cross Abstract: Identification of joint dependence among several random vectors plays an important role in many statistical applications, where the data may contain sensitive or confidential information.
arXiv:2601. 21959v2 Announce Type: replace-cross Abstract: We develop a near-optimal testing procedure under the framework of Gaussian differential privacy for simple as well as one- and two-sided tests under monotone likelihood ratio conditions.
arXiv:2606. 01908v1 Announce Type: new Abstract: Test-time adaptation (TTA) can reduce error on new and different data by updating the model on these inputs during inference.
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:2602. 01607v3 Announce Type: replace-cross Abstract: Differentially private synthetic data enables the sharing and analysis of sensitive datasets while providing rigorous privacy guarantees for individual contributors.
arXiv:2503. 10945v3 Announce Type: replace-cross Abstract: Current practices for reporting differential privacy (DP) guarantees for machine learning (ML) algorithms such as DP-SGD provide an incomplete and potentially misleading picture.