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Gradient Boosting Performs Gaussian Process Inference
Aleksei Ustimenko · Artem Beliakov · Liudmila Prokhorenkova
Keywords: [ Probabilistic Methods ] [ gradient boosting ] [ kernel gradient boosting ] [ gaussian process ] [ knowledge uncertainty ]
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.