Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution
By Yichen Zhou, Giles Hooker. 2018
14 citations recorded by the source corpus.
This paper examines a novel gradient boosting framework for regression. We regularize gradient boosted trees by introducing subsampling and employ a modified shrinkage algorithm so that at every boosting stage the estimate is given by an average of trees. The resulting algorithm, titled Boulevard, is shown to converge as the number of trees grows. We also demonstrate a central limit theorem for this limit, allowing a characterization of uncertainty for predictions. A simulation study and real world examples provide support for both the predictive accuracy of the model and its limiting behavior.
Subfields: Artificial Intelligence, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Stefan Wager
- Susan Athey
- Julie Tibshirani
- Rina Friedberg
- Guido W. Imbens
- Mihaela van der Schaar
- Ahmed M. Alaa
- Nathan Kallus
In Statistics and Probability
- Susan Athey
- Stefan Wager
- Julie Tibshirani
- Rina Friedberg
- Guido W. Imbens
- Mihaela van der Schaar
- Ahmed M. Alaa
- Victor Chernozhukov
Citations in the corpus
- Local Linear Forests (2018, background): “…by Basu et al. (2018), who iteratively grown feature-weighted tree ensembles that perform especially well for discovering interactions, Zhou and Hooker (2018), who considered a hybrid between random forests and boosting, and Zhu, Zeng, and Kosorok (2015), who did deeper search during splitting…”