1 citations · 1 across the 2 of their papers we have counts for
3 papers
stat.ML2022★ 1 cited
Learning Linear Non-Gaussian Polytree Models
Daniele Tramontano, Anthea Monod, Mathias Drton
In the context of graphical causal discovery, we adapt the versatile framework of linear non-Gaussian acyclic models (LiNGAMs) to propose new algorithms to efficiently learn graphs…
stat.ML2022
Rewiring Networks for Graph Neural Network Training Using Discrete Geometry
Jakub Bober, Anthea Monod, Emil Saucan +1
Information over-squashing is a phenomenon of inefficient information propagation between distant nodes on networks. It is an important problem that is known to significantly impac…
stat.ML2021
Curved Markov Chain Monte Carlo for Network Learning
John Sigbeku, Emil Saucan, Anthea Monod
We present a geometrically enhanced Markov chain Monte Carlo sampler for networks based on a discrete curvature measure defined on graphs. Specifically, we incorporate the concept…