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Found 3 papers, 2 papers with code
Choosing the parameter of the Fermat distance: navigating geometry and noise
The Fermat distance has been recently established as a useful tool for machine learning tasks when a natural distance is not directly available to the practitioner or to improve the results given by Euclidean distances by exploding the geometrical and statistical properties of the dataset.
Efficient adjustment sets in causal graphical models with hidden variables
Moreover, we show that if either no variables are hidden or if all the observable variables are ancestors of either treatment, outcome, or the variables that are used to decide treatment, a globally optimal adjustment set exists.
Nonhomogeneous Euclidean first-passage percolation and distance learning
Consider an i. i. d.
Probability 60D05, 62G99
