English(EN) XAI-Driven Confidence Estimation for OOD-Resilient Runtime Prediction

新的XAI框架提升了资源管理中ML模型的可靠性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了一种新颖的事后XAI框架——轻量级运行时可解释性（LEX），旨在提高基于机器学习的资源管理模型的可靠性。LEX通过使用高斯混合模型对训练输入空间嵌入进行聚类来解决分布外（OOD）输入问题，从而实现输入特定的误差界限。在对Intel Alder Lake处理器基于LSTM的IPS预测模型进行测试时，LEX在未见过的负载上表现出约94%的容纳率，并与现有的全局方法相比提供了约17%更紧密的置信度界限，同时运行时开销极小。 AI

影响增强了关键资源管理系统中ML模型的可靠性和可信度。

排序理由详细介绍用于ML模型韧性的新XAI框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Mahmoud Sanad, Mohammed Bakr Sikal, Osama Abboud, Heba Khdr, J\"org Henkel · 2026-11-11 05:00

面向OOD鲁棒运行时预测的XAI驱动置信度估计

arXiv:2611.13199v1 Announce Type: cross Abstract: Machine learning-based resource management (RM) models degrade under out-of-distribution (OOD) inputs at runtime, yet existing approaches in the literature rely on a single global error margin that is overly conservative and fails…

本文如何被排名

报道来源 [1]

面向OOD鲁棒运行时预测的XAI驱动置信度估计

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