🤖 AI Summary
本文解决了机器学习中分布偏移下的泛化问题，通过引入γ*-概念偏移和熵最优传输方法，提出了统一的误差界，并开发了能够估计这些偏移的算法。
📝 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 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.