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.

翻译：本文证明，基于对称决策树的梯度提升方法可等价地重新表述为一种核方法，该方法收敛于特定核岭回归问题的解。由此，我们获得了高斯过程后验均值的收敛性，进而可以轻松地将梯度提升转化为后验采样器，通过蒙特卡洛估计后验方差提供更好的知识不确定性估计。我们证明，所提出的采样器能够实现更优的知识不确定性估计，从而提升域外检测性能。