How to deal with missing data in supervised deep learning?

no code implementations • ICLR 2022 • Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen

To address supervised deep learning with missing values, we propose to marginalize over missing values in a joint model of covariates and outcomes.

Inductive Bias Variational Inference

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

1 code implementation • ICLR 2021 • Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen

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.

Variational Inference

Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!

1 code implementation • 2 May 2019 • Niels Bruun Ipsen, Lars Kai Hansen

It has been shown that learning signal structure in terms of principal components is dependent on the ratio of sample size and dimensionality and that a critical number of observations is needed before learning starts (Biehl and Mietzner, 1993).