一篇新发表在arXiv上的综述文章综合了跨学科的缺失数据插补研究。它将方法从经典统计学归类到现代深度学习技术，包括GANs、扩散模型和大型语言模型。该论文还探讨了插补与分类和异常检测等下游任务的整合，并确定了隐私保护插补和可泛化模型等未来研究方向。 AI
影响 提供了插补方法的全面概述，可能指导处理不完整数据集的AI系统的未来研究和开发。
排序理由 这是一篇关于特定机器学习主题的综述文章。
AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →
综述将统计插补方法与现代机器学习进展联系起来
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- 这是一篇关于特定机器学习主题的综述文章。
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报道来源 [1]
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缺失数据插补的跨学科跨任务综述
arXiv:2511.01196v3 Announce Type: replace Abstract: Missing data is a fundamental challenge in data science, significantly hindering analysis and decision-making across a wide range of disciplines, including healthcare, bioinformatics, social science, e-commerce, and industrial m…