English(EN) A Unified Perspective on Conformal Prediction and Wasserstein Distributionally Robust Optimization for Uncertainty Quantification

保形预测与DRO统一用于不确定性量化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一个统一的概率框架，将保形预测（CP）和分布鲁棒优化（DRO）联系起来用于不确定性量化。这种新视角将这两种方法都视为从有限校准数据中推导出数据依赖型分位数估计器的方式。虽然CP调整分位数水平，DRO移动分位数数值，但它们的构建方式不同，导致行为各异，尤其是在分数分布的尾部。 AI

影响为理解和潜在改进机器学习中的不确定性量化方法提供了一个统一的理论视角。

排序理由学术论文，提出了一个连接两种现有方法的新的理论框架。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Kehan Long, Yiqi Zhao, Pol Mestres, Lars Lindemann, Nikolay Atanasov, Jorge Cort\'es · 2026-09-01 04:00

统一视角下的保角预测与Wasserstein分布鲁棒优化用于不确定性量化

arXiv:2608.29789v1 Announce Type: cross Abstract: Uncertainty quantification from finite data is central to machine learning, optimization, and automation systems, where decisions must remain reliable under limited samples and test-time distribution shift. Conformal prediction (C…