新研究推进机器学习中的差分隐私以适应和测试

报道来源 [4]

1. arXiv cs.LG TIER_1 English(EN) · Zefeng Li, Qiaoyue Tang, Mathias Lecuyer, Evan Shelhamer · 2026-06-02 04:00

   Private and Stable Test-Time Adaptation with Differential Privacy

   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. However, these updates raise the issue of privacy w.r.t. the testing data, because the model parameters no…

2. arXiv cs.LG TIER_1 English(EN) · Ben Jacobsen, Tomas Gonzales, Gavin Brown, Kassem Fawaz, Aaditya Ramdas · 2026-05-29 04:00

   Optimal Rates for Differentially Private Hypothesis Testing with E-values

   arXiv:2605.28952v1 Announce Type: cross Abstract: E-values have attracted considerable interest in recent years as flexible tools for enabling anytime-valid and adaptive data analysis. Hypothesis testing is at the core of many of these applications, which can often involve privat…

3. Hugging Face Daily Papers TIER_1 English(EN) · 2026-05-27 18:00

   Optimal Rates for Differentially Private Hypothesis Testing with E-values

   E-values have attracted considerable interest in recent years as flexible tools for enabling anytime-valid and adaptive data analysis. Hypothesis testing is at the core of many of these applications, which can often involve private or sensitive data. In this work, we answer a sim…

4. arXiv stat.ML TIER_1 English(EN) · Yu-Wei Chen, Raghu Pasupathy, Jordan Awan · 2026-06-02 04:00

   简单和MLR假设的近乎最优的隐私测试

   arXiv:2601.21959v2 Announce Type: replace 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. Our mechanism is based on a private mean…