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stat.ML2026
Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Brian Liu, Rahul Mazumder, Peter Radchenko
Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficu…
stat.ML2025
FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML
Brian Liu, Rahul Mazumder
We present FAST, an optimization framework for fast additive segmentation. FAST segments piecewise constant shape functions for each feature in a dataset to produce transparent add…
stat.ML2025
Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests
Brian Liu, Rahul Mazumder
We study the often overlooked phenomenon, first noted in \cite{breiman2001random}, that random forests appear to reduce bias compared to bagging. Motivated by an interesting paper…