Gradient Boosting Performs Gaussian Process Inference
This provides a method for better uncertainty estimation in gradient boosting, addressing a key limitation for practitioners in machine learning.
The paper demonstrates that gradient boosting with symmetric decision trees can be reformulated as a kernel method converging to a Gaussian Process posterior mean, enabling Monte-Carlo sampling for uncertainty estimation, which improves out-of-domain detection.
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.