Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte CarloDownload PDF

Published: 31 Oct 2022, Last Modified: 22 Dec 2022NeurIPS 2022 AcceptReaders: Everyone
Keywords: Variational Autoencoders, Hamiltonian Monte Carlo
Abstract: Variational Autoencoders (VAEs) have recently been highly successful at imputing and acquiring heterogeneous missing data. However, within this specific application domain, existing VAE methods are restricted by using only one layer of latent variables and strictly Gaussian posterior approximations. To address these limitations, we present HH-VAEM, a Hierarchical VAE model for mixed-type incomplete data that uses Hamiltonian Monte Carlo with automatic hyper-parameter tuning for improved approximate inference. Our experiments show that HH-VAEM outperforms existing baselines in the tasks of missing data imputation and supervised learning with missing features. Finally, we also present a sampling-based approach for efficiently computing the information gain when missing features are to be acquired with HH-VAEM. Our experiments show that this sampling-based approach is superior to alternatives based on Gaussian approximations.
TL;DR: An improved Hierarchical VAE by means of tuned HMC for information acquisition from heterogeneous incomplete data.
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