not-MIWAE: Deep Generative Modelling with Missing not at Random Data

Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen

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Abstract

When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data. This allows for incorporating prior information about the type of missingness (e.g. self-censoring) into the model. Our inference technique, based on importance-weighted variational inference, involves maximising a lower bound of the joint likelihood. Stochastic gradients of the bound are obtained by using the reparameterisation trick both in latent space and data space. We show on various kinds of data sets and missingness patterns that explicitly modelling the missing process can be invaluable.
Original languageEnglish
Title of host publicationProceedings of 9th International Conference on Learning Representations
Number of pages18
Publication date2021
Publication statusPublished - 2021
Event9th International Conference on Learning Representations - Virtual event
Duration: 3 May 20217 May 2021

Conference

Conference9th International Conference on Learning Representations
LocationVirtual event
Period03/05/202107/05/2021

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