3 papers
stat.ML2026
Conditional Local Importance by Quantile Expectations
Kelvyn K. Bladen, Adele Cutler, D. Richard Cutler +1
Global variable importance measures are commonly used to interpret the results of machine learning models. Local variable importance techniques assess how variables contribute to i…
cs.GR2026
Random-Forest-Induced Graph Neural Networks for Tabular Learning
Haozhe Chen, Soheila Farokhi, Kelvyn Bladen +2
Graphs are essential for modeling complex relationships and capturing structured interactions in data. Graph Neural Networks (GNNs) are particularly effective when such relational…
math.ST2025
Mathematical Theory of Collinearity Effects on Machine Learning Variable Importance Measures
Kelvyn K. Bladen, D. Richard Cutler, Alan Wisler
In many machine learning problems, understanding variable importance is a central concern. Two common approaches are Permute-and-Predict (PaP), which randomly permutes a feature in…