@inproceedings{
kori2023a,
title={A Causal Ordering Prior for Unsupervised Representation Learning},
author={Avinash Kori and Pedro Sanchez and Konstantinos Vilouras and Ben Glocker and Sotirios A. Tsaftaris},
booktitle={Causal Representation Learning Workshop at NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=1gY17EWQyl}
}
Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables. Causal representation learning (CRL), however, argues that factors of variation in a dataset are, in fact, causally related. Allowing latent variables to be correlated, as a consequence of causal relationships, is more realistic and generalisable. So far, provably identifiable methods rely on: auxiliary information, weak labels, and interventional or even counterfactual data. Inspired by causal discovery with functional causal models, we propose a fully unsupervised representation learning method that considers a data generation process with a latent additive noise model (ANM). We encourage the latent space to follow a causal ordering via loss function based on the Hessian of the latent distribution.
A Causal Ordering Prior for Unsupervised Representation Learning
Avinash Kori Pedro Sanchez Konstantinos Vilouras Ben Glocker Sotirios A. Tsaftaris