33 citations · 38 across the 7 of their papers we have counts for
3 papers · 1 filter
Directed Graph Embeddings in Pseudo-Riemannian Manifolds
Aaron Sim, Maciej Wiatrak, Angus Brayne +2
The inductive biases of graph representation learning algorithms are often encoded in the background geometry of their embedding space. In this paper, we show that general directed…
Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness
Adam Foster, Árpi Vezér, Craig A Glastonbury +3
Learning meaningful representations of data that can address challenges such as batch effect correction and counterfactual inference is a central problem in many domains including…
Random Forests on Distance Matrices for Imaging Genetics Studies
Aaron Sim, Dimosthenis Tsagkrasoulis, Giovanni Montana
We propose a non-parametric regression methodology, Random Forests on Distance Matrices (RFDM), for detecting genetic variants associated to quantitative phenotypes representing th…