Skip to yearly menu bar Skip to main content


MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets

Pierre-Alexandre Mattei · Jes Frellsen

Pacific Ballroom #9

Keywords: [ Unsupervised Learning ] [ Dimensionality Reduction ] [ Deep Generative Models ] [ Bayesian Deep Learning ] [ Approximate Inference ]


We consider the problem of handling missing data with deep latent variable models (DLVMs). First, we present a simple technique to train DLVMs when the training set contains missing-at-random data. Our approach, called MIWAE, is based on the importance-weighted autoencoder (IWAE), and maximises a potentially tight lower bound of the log-likelihood of the observed data. Compared to the original IWAE, our algorithm does not induce any additional computational overhead due to the missing data. We also develop Monte Carlo techniques for single and multiple imputation using a DLVM trained on an incomplete data set. We illustrate our approach by training a convolutional DLVM on incomplete static binarisations of MNIST. Moreover, on various continuous data sets, we show that MIWAE provides extremely accurate single imputations, and is highly competitive with state-of-the-art methods.

Live content is unavailable. Log in and register to view live content