阅读：General Quantification of Covariate and Concept Shifts

• 模式: 沉浸阅读高引用论文 (roll=41)
• 时间: 2026-09-11 23:10:29

阅读记录：General Quantification of Covariate and Concept Shifts

• 来源: arxiv | 年份: N/A | 引用: 0 | 链接: http://arxiv.org/abs/2609.11918v1

论文摘要（原始）

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 the gap between theory and practical applications. We first show that existing definition of concept shift breaks when the source and target supports mismatch. Leveraging entropic optimal transport, we propose a key notion: $γ^{}\!$-concept shifts, and derive a general error bound unifying covariate and $γ^{}\!$-concept shifts, which applies to broad loss functions, label spaces, and stochastic labeling. We further develop estimators for these shifts with concentration guarantees, and the DataShifts algorithm, which can quantify distribution shifts and estimate the error bound in most applications - a rigorous and general tool for analyzing learning error under distribution shift.

AI 概括

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1. 背景痛点：理论与现实“两层皮”

现有的分布偏移泛化理论，要么假设源域目标域支撑集完全重合（这哪可能嘛，现实数据哪有这么整齐），要么只能处理特定损失函数、特定标签空间，搞得“理论归理论，实践归实践”——指标算不出来，界估不准，一到工程上就瞎蒙。作者一上来就甩了个大实话：连 concept shift（概念漂移）的定义在支撑集不匹配时都会崩，这地基不打牢，上层建筑全白搭。

2. 核心招式：把最优传输（OT）请进门，重新定义“概念漂移”

既然支撑集对不齐，那就用 Entropic Optimal Transport（熵正则化最优传输） 给它们“搭桥”。作者定义了 $\gamma^{}$-concept shift：不再死磕 $P(Y|X)$ 点对点的差，而是看联合分布 $P(X,Y)$ 在最优传输计划 $\gamma^{}$ 下的位移代价。这一招妙就妙在：

* 统一了 covariate shift 和 concept shift——一个公式吃遍天；

* 不挑损失函数、不挑标签空间、连随机标注都能管；

* 推导出的泛化误差界 既紧致又可估计，终于把理论从“象牙塔”拉到了“工地上”。

3. 落地工具：DataShifts 算法，给工程师递把“尺子”

光有界不行，还得能算。作者设计了 DataShifts 算法，基于 Sinkhorn 迭代把 $\gamma^{*}$ 算出来，再配上浓度不等式给估计量发“保