English(EN) General Quantification of Covariate and Concept Shifts

新的DataShifts算法量化机器学习分布偏移

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-11 04:00

研究人员开发了一种新的方法来量化机器学习中的协变量和概念偏移，解决了现有理论的局限性。该方法称为DataShifts，使用熵最优传输来统一和估计这些偏移，提供适用于各种损失函数和标签方案的通用误差界限。该算法为分析分布偏移下的学习误差提供了一个严谨的工具，弥合了理论界限与实际应用之间的差距。 AI

影响为理解和减轻机器学习模型的泛化误差提供了更鲁棒的理论框架。

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arXiv stat.ML TIER_1 English(EN) · Hongbo Chen, Li Charlie Xia · 2026-09-11 04:00

协变量和概念偏移的通用量化

arXiv:2609.11918v1 Announce Type: cross Abstract: Generalization under distribution shift remains a core challenge in modern machine learning, yet existing learning bound theory is limited to narrow, idealized settings and is non-estimable from samples. In this paper, we bridge t…