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.
|Title of host publication||Proceedings of 9th International Conference on Learning Representations|
|Number of pages||18|
|Publication status||Published - 2021|
|Event||9th International Conference on Learning Representations - Virtual event|
Duration: 3 May 2021 → 7 May 2021
|Conference||9th International Conference on Learning Representations|
|Period||03/05/2021 → 07/05/2021|