41 citations · 68 across the 9 of their papers we have counts for
5 papers · 1 filter
Causal Ordering for Structure Learning from Time Series
Pedro P. Sanchez, Damian Machlanski, Steven McDonagh +1
Predicting causal structure from time series data is crucial for understanding complex phenomena in physiology, brain connectivity, climate dynamics, and socio-economic behaviour.…
A Causal Ordering Prior for Unsupervised Representation Learning
Avinash Kori, Pedro Sanchez, Konstantinos Vilouras +2
Unsupervised representation learning with variational inference relies heavily on independence assumptions over latent variables. Causal representation learning (CRL), however, arg…
Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models
Virginia Fernandez, Pedro Sanchez, Walter Hugo Lopez Pinaya +3
Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself. We introduce Privacy Distillation, a framework that allow…
Causal Machine Learning for Healthcare and Precision Medicine
Pedro Sanchez, Jeremy P. Voisey, Tian Xia +3
Causal machine learning (CML) has experienced increasing popularity in healthcare. Beyond the inherent capabilities of adding domain knowledge into learning systems, CML provides a…
Diffusion Causal Models for Counterfactual Estimation
Pedro Sanchez, Sotirios A. Tsaftaris
We consider the task of counterfactual estimation from observational imaging data given a known causal structure. In particular, quantifying the causal effect of interventions for…