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Adaptive optimization for prediction with missing data

delete2025-03-24
delete0
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OA
AI
D
Dimitris Bertsimas
A
Arthur Delarue *
J
Jean Pauphilet
DOI：10.1007/s10994-025-06757-6delete
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摘要

摘要

En 中文
在训练具有缺失条目的数据上的预测模型时，最广泛使用且通用的方法是管道技术，即首先插补缺失条目，然后计算预测结果。本文将带缺失数据的预测视为一个两阶段自适应优化问题，并提出一类新的模型——自适应线性回归模型，其中回归系数会根据观测特征集进行调整。我们证明某些自适应线性回归模型等价于同时学习插补规则和下游线性回归模型，而非按顺序学习。我们利用这种联合插补-再回归的解释来推广我们的框架至非线性模型。在数据严重非随机缺失的设置下，我们的方法实现了2-10%的样本外准确率提升。
Keyword:
Missing data
Adaptive optimization

期刊

Machine Learning 封面图
Machine Learning
IF:
2.9
论文数:
2.7K
被引数:
3.6W

机构

M
MIT
学者数:
25
论文数: 10
被引数: 86
引用论文

引用论文

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Adaptive optimization for prediction with missing data缺失数据下预测的自适应优化
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IF2.9
err2025-03-24
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errOAAI
errBertsimas, Dimitris; Delarue, Arthur; Pauphilet, Jean
err分享
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