99 citations · 157 across the 39 of their papers we have counts for
6 papers · 1 filter
-Means Clustering for Persistent Homology
Yueqi Cao, Prudence Leung, Anthea Monod
Persistent homology is a methodology central to topological data analysis that extracts and summarizes the topological features within a dataset as a persistence diagram; it has re…
Fast Topological Signal Identification and Persistent Cohomological Cycle Matching
Inés García-Redondo, Anthea Monod, Anna Song
Within the context of topological data analysis, the problems of identifying topological significance and matching signals across datasets are important and useful inferential task…
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…
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…
A Geometric Condition for Uniqueness of Fréchet Means of Persistence Diagrams
Yueqi Cao, Anthea Monod
The Fréchet mean is an important statistical summary and measure of centrality of data; it has been defined and studied for persistent homology captured by persistence diagrams. Ho…
Approximating Persistent Homology for Large Datasets
Yueqi Cao, Anthea Monod
Persistent homology is an important methodology in topological data analysis which adapts theory from algebraic topology to data settings. Computing persistent homology produces pe…