Reliable Risk Estimation with Kernel Mean Matching under Dependent Sampling

Abstract:Kernel Mean Matching (KMM) estimates risk under covariate shift by reweighting labelled source samples so that their kernel mean embedding matches that of unlabeled target samples. Existing finite-sample guarantees are usually stated for independent samples, although many applications involve temporal, spatial, or clustered dependence. We give a finite-sample KMM analysis under dependent sampling by replacing nominal sample sizes with effective sample sizes in the concentration steps used by the classical proof. The approach uses Bernstein inequalities for geometrically strongly mixing processes and derives a model-based effective sample size. The resulting bound separates overlap, dependence, and smoothness effects with specific constants derived under a Gaussian process assumption. This analysis yields an additional tradeoff: smoother losses preserve more input dependence and therefore reduce effective sample size, while also reducing function complexity and empirical-to-population risk gaps. Applying the result to prediction yields confidence intervals that achieve near-nominal coverage in synthetic experiments and substantially reduce undercoverage on real monitoring data.

Machine Learning (cs.LG)

arXiv:2610.18292 [cs.LG]

(or arXiv:2610.18292v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.18292 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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