English(EN) Predictively Oriented Gaussian Process Posteriors

新的高斯过程方法增强了预测不确定性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-05 04:00

研究人员引入了面向预测的高斯过程（PrO-GPs），这是一种新颖的高斯过程方法，旨在改进预测不确定性量化。与需要仔细设计选择（如核选择）的标准GP不同，PrO-GPs将预测不确定性作为主要的推断目标。虽然非参数模型的PrO后验的直接计算是难以处理的，但研究人员开发了一种有效的采样方案和一种简化形式。在合成数据和真实数据上的实验表明，与传统的GP方法相比，当模型被错误指定时，PrO-GPs提供了更好校准的预测分布。 AI

影响为模型提供了改进的不确定性量化，有可能在关键应用中带来更可靠的AI系统。

排序理由详细介绍机器学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Callum Lau, Jeremias Knoblauch, Louis Sharrock · 2026-10-05 04:00

预测导向的高斯过程后验

arXiv:2610.03201v1 Announce Type: cross Abstract: Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitioners to make a number of design decisions, such as the choice of the kernel and the…

本文如何被排名

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预测导向的高斯过程后验

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