Establishing H-Consistency Guarantees for Regression Surrogate Losses

This paper presents the first in-depth study of H-consistency bounds for regression, establishing non-asymptotic guarantees for the squared loss with respect to various surrogate regression losses such as Huber loss, ℓp losses, and squared ε-insensitive loss. The analysis leverages new generalized theorems for establishing H-consistency bounds.