English(EN) Private Online Prediction from Experts with Small Losses

新算法为私有在线专家预测提供近乎最优的遗憾

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 11:18

研究人员开发了一种新的差分隐私在线专家预测算法。该算法实现了 \tilde{O}(\sqrt{L^\star}) 的遗憾界限，在 $L^\star$ 代表最佳专家累积损失的低损失情况下，相比现有方法有了显著改进。新的界限与非私有算法相当，表明隐私成本很小。然而，该算法的性能取决于输入序列是否是无意识的，并且对于自适应输入，\tilde{\Omega}(\sqrt{T}) 的遗憾是不可避免的。 AI

影响这项研究推动了差分隐私机器学习领域的发展，有望实现更安全、更高效的专家预测系统。

排序理由该项目是一篇研究论文，详细介绍了一种用于机器学习问题的新算法。[lever_c_demoted from research: ic=1 ai=1.0]

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-21 11:18

专家小额损失的私人在线预测

We consider online prediction from experts, a fundamental problem in machine learning, under differential privacy constraints. Existing private algorithms achieve near-optimal regret $\tilde{O}(\sqrt{T})$, where $T$ is the time horizon. However, this bound becomes suboptimal when…

本文如何被排名

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专家小额损失的私人在线预测

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