2 papers
stat.ML2024
Alpha-Trimming: Locally Adaptive Tree Pruning for Random Forests
Nikola Surjanovic, Andrew Henrey, Thomas M. Loughin
We demonstrate that adaptively controlling the size of individual regression trees in a random forest can improve predictive performance, contrary to the conventional wisdom that t…
cs.LG2023
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…