A Generalized Iterative Imputation Framework for Model Adaptation and Oracle Feature Utilization
Hao Wang, zhengnan li, Zhichao Chen, Xu Chen, Shuting He, Guangyi Liu, Haoxuan Li, Zhouchen Lin
Iterative imputation is a prevalent method for completing missing data, which involves iteratively imputing each feature by treating it as a target variable and predicting its missing values using the remaining features.  However, existing iterative imputation methods exhibit two critical defects: (1) model misspecification, where a uniform parametric form of model is applied across different features, conflicting with heterogeneous data generation processes; (2) underutilization of fully observed features, where all features are treated as potentially missing, neglecting the valuable information in fully observed features.In this work, we propose Kernel Point Imputation (KPI), a bi-level optimization framework designed to address these issues. The inner-level optimization optimizes the model form for each feature in a reproducing kernel Hilbert space, mitigating model misspecification. The outer-level optimization leverages fully observed features as supervision signals to refine imputations. Extensive experiments on real-world datasets demonstrate that KPI consistently outperforms state-of-the-art imputation methods.