XBART: A Scalable Stochastic Algorithm for Supervised Machine Learning with Additive Tree Ensembles

Jingyu He
This dissertation develops a novel stochastic tree ensemble method for nonlinear regression, which I refer to as XBART, short for Accelerated Bayesian Additive Regression Trees. By combining regularization and stochastic search strategies from Bayesian modeling with computationally efficient techniques from recursive partitioning approaches, the new method attains state-of-the-art performance: in many settings it is both faster and more accurate than the widely-used XGBoost algorithm. Via careful simulation studies, I demonstrate that our new approach provides...
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