Portrait of Vitoria Barin Pacela

Vitoria Barin Pacela

PhD - Université de Montréal
Supervisor
Simon Lacoste-Julien
Research Topics
Causality
Deep Learning
Out-of-Distribution (OOD) Generalization
Probabilistic Models
Representation Learning
Robustness
Website
Google Scholar
GitHub

Publications

On the Identifiability of Quantized Factors
Vitoria Barin Pacela
Kartik Ahuja
Simon Lacoste-Julien
Pascal Vincent
Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the… (see more) theory of identifiability. The identifiability of independent latent factors is proven to be impossible in the unsupervised i.i.d. setting under a general nonlinear map from factors to observations. In this work, however, we demonstrate that it is possible to recover quantized latent factors under a generic nonlinear diffeomorphism. We only assume that the latent factors have independent discontinuities in their density, without requiring the factors to be statistically independent. We introduce this novel form of identifiability, termed quantized factor identifiability, and provide a comprehensive proof of the recovery of the quantized factors.
2024-03-15
Proceedings of the Third Conference on Causal Learning and Reasoning (published)
proceedings.mlr.press
arxiv.org
On the Identifiability of Quantized Factors
Vitoria Barin Pacela
Kartik Ahuja
Simon Lacoste-Julien
Pascal Vincent
Disentanglement aims to recover meaningful latent ground-truth factors from the observed distribution solely, and is formalized through the … (see more)theory of identifiability. The identifiability of independent latent factors is proven to be impossible in the unsupervised i.i.d. setting under a general nonlinear map from factors to observations. In this work, however, we demonstrate that it is possible to recover quantized latent factors under a generic nonlinear diffeomorphism. We only assume that the latent factors have independent discontinuities in their density, without requiring the factors to be statistically independent. We introduce this novel form of identifiability, termed quantized factor identifiability, and provide a comprehensive proof of the recovery of the quantized factors.
2023-06-28
ArXiv (preprint)
arxiv.org
arxiv.org