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English(EN) A Unified Framework for Training-Dataset Aggregation and Exact Machine Unlearning via Gibbs Probability Measures

新研究探讨机器学习的统一框架 · 追踪到2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

两篇新研究论文探讨了机器学习的进展，机器学习旨在从训练好的AI模型中移除特定数据的影响。第一篇论文介绍了一个用于聚合训练数据集和使用吉布斯概率测度实现精确机器学习的统一框架，并应用于联邦学习。第二篇论文提出了SALA，一个样本粒度的优化框架，允许近似机器学习方法在单个样本级别上运行，提供可证明的每样本界限并提高效率。 AI

影响 这些进展可能导致更有效和精确地从AI模型中移除数据，增强隐私和安全。

排序理由 两篇发表在arXiv上的学术论文，详细介绍了机器学习的新方法。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新研究探讨机器学习的统一框架 · 追踪到2个来源

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两篇发表在arXiv上的学术论文，详细介绍了机器学习的新方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Yaiza Bermudez, Samir M. Perlaza, I\~naki Esnaola ·

    通过吉布斯概率测度实现训练数据集聚合与精确机器学习不可知论的统一框架

    arXiv:2611.05544v1 Announce Type: cross Abstract: In this paper, three operations on Gibbs probability measures are studied: renormalization, normalized log-linear combination, and nesting. The resulting measures are shown to be Gibbs probability measures with explicit expression…

  2. arXiv cs.LG TIER_1 English(EN) · Yaxin Xiao, Yulin Jin, Li Tang, Haoyang LI, Huadi Zheng, Qingqing Ye, Haibo Hu ·

    Sample-Grained Approximate Unlearning with Provable Per-Sample Bounds

    arXiv:2611.04501v2 Announce Type: replace Abstract: Machine unlearning aims to erase the influence of a designated forget set from a trained model while preserving strong utility on the retain set. Approximate unlearning methods efficiently achieve this goal without full retraini…

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