AI RESEARCH
Kernel Debiased Plug-in Estimation based on the Universal Least Favorable Submodel
arXiv CS.LG
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ArXi:2603.08945v1 Announce Type: cross We propose ULFS-KDPE, a kernel debiased plug-in estimator based on the universal least favorable submodel, for estimating pathwise differentiable parameters in nonparametric models. The method constructs a data-adaptive debiasing flow in a reproducing kernel Hilbert space (RKHS), producing a plug-in estimator that achieves semiparametric efficiency without requiring explicit derivation or evaluation of efficient influence functions.