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Niels Bruun Ipsen

2 accepted papers

2022

How to deal with missing data in supervised deep learning?

ICLR 2022poster

The issue of missing data in supervised learning has been largely overlooked, especially in the deep learning community. We investigate strategies to adapt neural architectures for handling missing values. Here, we focus on regression and classification problems where the features are assumed to be…

Cited by 50SourcePDFScholar
2021

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

ICLR 2021poster

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 th…