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高斯分布中具有 Huber 污染的近最优算法：均值估计与线性回归

💡 原文中文，约400字，阅读约需1分钟。
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内容提要

本研究探讨了在存在重尾污染的情况下，强鲁棒回归估计器的高维特性。结果表明，优化调整的Huber损失与位置参数δ是次优的，需要进一步正则化以达到最佳性能。此外，研究还导出了岭回归的超额风险的衰减速率。研究展示了公式可以推广到更丰富的模型和数据分布。

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标签

Huber损失 岭回归 强鲁棒回归估计器 正则化 算法 重尾污染
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