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

Niels Bruun Ipsen 1 Pierre-Alexandre Mattei 2, 3 Jes Frellsen 1
2 MAASAI - Modèles et algorithmes pour l’intelligence artificielle
CRISAM - Inria Sophia Antipolis - Méditerranée , UNS - Université Nice Sophia Antipolis (... - 2019), JAD - Laboratoire Jean Alexandre Dieudonné, Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
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.
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Contributor : Pierre-Alexandre Mattei Connect in order to contact the contributor
Submitted on : Monday, December 7, 2020 - 4:45:03 PM
Last modification on : Tuesday, January 4, 2022 - 6:02:28 AM

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  • HAL Id : hal-03044124, version 1
  • ARXIV : 2006.12871


Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen. not-MIWAE: Deep Generative Modelling with Missing not at Random Data. ICLR 2021 - International Conference on Learning Representations, May 2021, Virtual, Austria. ⟨hal-03044124⟩



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