Gradient Boosting Performs Gaussian Process Inference¶
Authors: Aleksei Ustimenko, Artem Beliakov, Liudmila Prokhorenkova
Published: 2022 ()
Algorithm: Gradient Boosting
arXiv: 2206.05608
Summary¶
Abstract¶
This paper shows that gradient boosting based on symmetric decision trees can be equivalently reformulated as a kernel method that converges to the solution of a certain Kernel Ridge Regression problem. Thus, we obtain the convergence to a Gaussian Process' posterior mean, which, in turn, allows us to easily transform gradient boosting into a sampler from the posterior to provide better knowledge uncertainty estimates through Monte-Carlo estimation of the posterior variance. We show that the proposed sampler allows for better knowledge uncertainty estimates leading to improved out-of-domain detection.