Introduces a Hilbert-valued one-step estimator for the kernel covariance operator between covariates and residuals that enables semiparametrically efficient inference on noise heterogeneity and residual independence in additive noise models.
On the hardness of conditional independence testing in practice.arXiv preprint arXiv:2512.14000,
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Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
Introduces a Hilbert-valued one-step estimator for the kernel covariance operator between covariates and residuals that enables semiparametrically efficient inference on noise heterogeneity and residual independence in additive noise models.