论文标题
梯度提升执行高斯工艺推理
Gradient Boosting Performs Gaussian Process Inference
论文作者
论文摘要
本文表明，基于对称决策树的梯度提升可以等效地重新重新重新构成内核方法，该方法会收敛到某个内核脊回归问题的解决方案。因此，我们获得了融合到高斯过程的后均值，这又使我们可以轻松地将梯度提升到从后部转化为采样器，从而通过对后方差的蒙特卡洛估计来提供更好的知识不确定性估计。我们表明，提出的采样器允许更好的知识不确定性估计，从而改善了域外检测。
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.
