HynetImpute: Missing Pattern Specialized Imputation via Hypernetwork

Missingness in real-world data often arises in complex patterns, with different subsets of features missing across samples due to diverse factors. Conventional imputation methods mostly learn a single model to cater for all possible missing patterns. However, missing patterns carry specific information which can be leveraged to guide better imputation via specialization. To this end, we propose HynetImpute which leverages a hypernetwork to represent a family of variational autoencoders (VAEs) for missing pattern specialized imputation. Specifically, HynetImpute uses the hypernetwork to generate the encoder parameters of a VAE “specialized” for the input on the fly according to its missing pattern. The encoded input is then fed to a decoder which is shared among all missing patterns to encourage consistent imputation across diverse missing scenarios. Together with the loss function designed for training, HynetImpute can achieve robust imputation results and imputed data distribution consistent with the ground truth distribution and invariant to missing patterns. Through extensive experiments on synthetic, UCI and real-world datasets, HynetImpute is demonstrated to outperform the state-of-the-art baseline methods significantly in terms of imputation accuracy and robustness, effectively capturing the nuances of diverse missing data scenarios.