SOURCE-LINKED INTELLIGENCE

General Quantification of Covariate and Concept Shifts

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

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,

Read original source ↗ Open in workspace

recordType

paper

region

Global