{"ID":2841535,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.11003","arxiv_id":"2511.11003","title":"Learning bounds for doubly-robust covariate shift adaptation","abstract":"Distribution shift between the training domain and the test domain poses a key challenge for modern machine learning. An extensively studied instance is the \\emph{covariate shift}, where the marginal distribution of covariates differs across domains, while the conditional distribution of outcome remains the same. The doubly-robust (DR) estimator, recently introduced by \\cite{kato2023double}, combines the density ratio estimation with a pilot regression model and demonstrates asymptotic normality and $\\sqrt{n}$-consistency, even when the pilot estimates converge slowly. However, the prior arts has focused exclusively on deriving asymptotic results and has left open the question of non-asymptotic guarantees for the DR estimator. This paper establishes the first non-asymptotic learning bounds for the DR covariate shift adaptation. Our main contributions are two-fold: (\\romannumeral 1) We establish \\emph{structure-agnostic} high-probability upper bounds on the excess target risk of the DR estimator that depend only on the $L^2$-errors of the pilot estimates and the Rademacher complexity of the model class, without assuming specific procedures to obtain the pilot estimate, and (\\romannumeral 2) under \\emph{well-specified parameterized models}, we analyze the DR covariate shift adaptation based on modern techniques for non-asymptotic analysis of MLE, whose key terms governed by the Fisher information mismatch term between the source and target distributions. Together, these findings bridge asymptotic efficiency properties and a finite-sample out-of-distribution generalization bounds, providing a comprehensive theoretical underpinnings for the DR covariate shift adaptation.","short_abstract":"Distribution shift between the training domain and the test domain poses a key challenge for modern machine learning. An extensively studied instance is the \\emph{covariate shift}, where the marginal distribution of covariates differs across domains, while the conditional distribution of outcome remains the same. The d...","url_abs":"https://arxiv.org/abs/2511.11003","url_pdf":"https://arxiv.org/pdf/2511.11003v1","authors":"[\"Jeonghwan Lee\",\"Cong Ma\"]","published":"2025-11-14T06:46:23Z","proceeding":"math.ST","tasks":"[\"math.ST\",\"econ.EM\",\"stat.ML\"]","methods":"[]","has_code":false}